Multi-behavior recommendation enriches user preference modeling by incorporating diverse auxiliary interactions. However, most existing methods simply treat interactions without target behaviors as absolute negative feedback. This strategy ignores an important intermediate state known as user hesitation, where users exhibit strong intent but fail to complete the final conversion due to various reasons. Consequently, models cannot distinguish true disinterest from intended but hesitant behavior, which introduces substantial noise into preference modeling. To address this issue, we propose a novel framework named Calibrated Hesitation Analysis for Multi-Behavior Recommendation via Mutual Information Maximization (CHARM). Specifically, we aggregate auxiliary behaviors that lead to successful conversions into latent intent representations and train an inference network by maximizing the mutual information between these intents and observed target behaviors. We then apply this network to auxiliary behaviors without conversion, under the assumption that the conversion had occurred, in order to infer latent conversion probabilities and identify high-intent hesitation candidates. Furthermore, to distinguish genuine hesitation from interaction termination caused by competing item choices, we design a competitor substitution penalty strategy to refine hesitation confidence scores. Finally, the calibrated hesitation set is incorporated into the recommendation process to improve ranking quality. Extensive experiments on three real-world datasets demonstrate that CHARM consistently outperforms existing state-of-the-art methods. The source code is available at https://github.com/city59/CHARM.

This paper addresses the problem of encryption-decryption-based bipartite synchronization control for a class of discrete-time coupled neural networks, in which the nodes exhibit both cooperative and antagonistic interactions. Initially, a Markov chain with concealed operating modes is employed to describe Markov jump coupled neural networks with switching topologies. In this framework, a hidden Markov model is incorporated, whose emission values express the system mode. Next, the decentralized adaptive event-triggered strategy is proposed to alleviate the communication burden caused by interactions between nodes. Moreover, an encryption-decryption algorithm that takes into account identity authentication is programmed to encrypt the data at the triggering moment of each node, thereby securing the data interaction privacy. Then, the observation mode-based bipartite synchronization control law is formulated to fulfill the control demands of the plant. Furthermore, some sufficient conditions for the networks to be mean square synchronized and satisfy theH∞performance are obtained based on the Lyapunov stability theory. At last, two simulation examples involving chaotic neural networks are presented to verify the effectiveness of the proposed method.

In this paper, a novel hidden semi-Markov model (HS-MM) in the continuous-time domain is proposed, and theH∞static output feedback control problem for hidden semi-Markov jump linear systems (HS-MJLSs) is investigated. Data transmission via a wireless network medium in HS-MJLSs is vulnerable to cyber attacks. Therefore, we adopt a resilient control strategy to ensure the stability of HS-MJLSs under hybrid cyber attacks, factoring in both denial-of-service attacks and false data injection attacks. In comparison with the conventional Markov jump linear systems, the HS-MM proposed by us is more universal, which is reflected in the following two aspects: (i) The system mode is considered to be undetectable. The jumping of the controller depends on the observation mode, and is asynchronous with the jumping of the system mode. (ii) Both the mode jumps of the original system and controller are considered to be related to the sojourn-time, which is more reasonable than the memoryless jumping processes. By using the established HS-MM and a Lyapunov-based approach, some sufficient conditions are obtained to ensure the stochastic stability of the HS-MJLSs with anH∞performance and hybrid cyber attacks. Finally, the proposed theoretical results are demonstrated by two simulation examples.

This paper investigates the practical finite-time deployment for heterogeneous partially unknown multi-agent systems (MASs), comprising first-order agents (FOAs) and second-order agents (SOAs). In the considered system, FOAs and SOAs are required to deploy onto two distinct spatial curves, regarded as a group deployment task, and the intergroup relationship between them is modeled by an unknown term. By designing appropriate network communication protocols (NCPs), the system dynamics are transformed into a partial differential equation (PDE) model. Then, under both full-domain and point-wise measurement schemes, two fuzzy systems are constructed to approximate the unknown term. Accordingly, two adaptive fuzzy controllers are developed to compensate for the unknown term and facilitate the desired deployment. Subsequently, by employing the Lyapunov functional method and inequality techniques, sufficient conditions are derived as linear matrix inequalities (LMIs) to guarantee the practical finite-time deployment. Finally, numerical simulations in a UAV-UGV cooperative scenario under both measurement schemes are presented, demonstrating the effectiveness and robustness of the proposed control strategy.

This paper investigates the resilient distributed filtering problem for networked Markov jump systems with time-varying delays under bandwidth constraints. In practical Internet of Things environments, limited communication resources and adversarial disruptions can jointly degrade estimation performance. To explicitly handle the trade-off between communication efficiency and estimation accuracy under adversarial conditions, a communication-aware, data-importance-driven encoding mechanism is proposed. By dynamically adjusting the quantization density and range according to real-time measurement importance, the proposed mechanism gives higher transmission priority to critical information, thereby improving the effective signal-to-noise ratio without increasing the overall communication burden. Moreover, an attack-dependent distributed filtering scheme is developed to counter data-importance-aware denial-of-service attacks, where the filter gains are adaptively reconfigured according to the attacked sensor nodes and system modes. Numerical simulations and a pulse-width-modulation-driven boost converter example verify the effectiveness of the proposed approach. In particular, compared with existing methods, the proposed mechanism improves the mean signal-to-noise ratio by 12 dB and reduces the average bandwidth consumption by 25% (3 bits/transmission versus 4 bits/transmission), while still maintaining a low estimation error (MSE = 0.0034) under targeted DoS attacks.

We study distributed principal component analysis (PCA) for large-scale federated data when the sample size $n$n and dimension $d$d are both ultra-large. This type of data is currently very common, but faces numerous challenges in PCA learning, such as communication overhead and computational complexity. We develop a new algorithm ${\mathsf {FedFask}}$FedFask (Fast Sketching for Federated learning) with lower communication cost $O(dr)$O(dr) and lower computational complexity $O(d(np/m+p^{2}+r^{2}))$O(d(np/m+p2+r2)), where $m$m is the number of workers, $r$r is the rank of matrix, $p$p is the dimension of sketched column space, and $r\leq p\ll d$r≤p≪d. In ${\mathsf {FedFask}}$FedFask, we adopt and develop technologies such as fast sketching, alignments with orthogonal Procrustes Fixing, and matrix Stiefel manifold via Kolmogorov-Nagumo-type average. Thus, ${\mathsf {FedFask}}$FedFask has a higher accuracy, lower stochastic variation, and best representation of multiple randomly projected eigenspaces, and avoids the orthogonal ambiguity of eigenspaces. We show that ${\mathsf {FedFask}}$FedFask achieves the same rate of learning $O\left(\frac{\kappa _{r}r}{\lambda _{r}}\sqrt{\frac{r^{*}}{n}}\right)$Oκrrλrr*n as the centralized PCA uses all data, and tolerates more workers to parallel acceleration computation. We conduct extensive experiments to demonstrate the effectiveness of ${\mathsf {FedFask}}$FedFask.

Controlling discrete-time linear systems with unknown model information while reducing control costs poses considerable obstacles. To tackle this issue, this paper proposes two novel data-driven impulsive control (IMC) frameworks for discrete-time linear systems. First, unlike most existing IMC strategies that rely on accurate system models or system identification, the proposed methods can directly design controllers based on measured data. Moreover, for data-driven IMC schemes that respect the average impulsive interval, the exponential stabilization of the system is demonstrated under the strong rank condition by leveraging relevant data and invoking the concepts of Lyapunov functions and the iterative synthesis procedure. Second, to reduce unnecessary impulsive actions during information transmission and thereby enhance control performance, a data-driven event-triggered impulsive control (ETIMC) approach is examined by combining event-triggered control with IMC. To mitigate the requirement for persistent excitation of input data, the concept of data informativity is utilized for analysis, and sufficient criteria for system stabilization under the proposed data-driven ETIMC are established in conjunction with the collected data. Ultimately, the validity of the theoretical results is verified through two illustrative examples.

In this paper, the design of a fuzzy iterative learning tracking control scheme is investigated for a class of uncertain strict-feedback nonlinear systems. A key contribution of this paper is to handle composite uncertainties including varying trial lengths, unknown nonlinearities, and an unknown time-varying parameter. Firstly, a time-axis extension mechanism is introduced to deal with data discontinuities caused by variable trial lengths. Additionally, fuzzy logic systems are employed to approximate unknown nonlinearities, while sliding mode control is utilized to compensate for approximation errors. Furthermore, both original and projection-based learning laws are developed to estimate the unknown time-varying parameter. Notably, the dynamic surface control technique is adopted to construct error propagation channels for ensuring accurate trajectory tracking. Finally, on a representative electromechanical system model, simulation results are presented to demonstrate the effectiveness of the proposed control strategy.

This paper studies the finite-time multistability of impulsive Hopfield neural networks with a general class of activation functions. First, the existence of Πni=1(2Mi+1) equilibrium points and Πni=1(Mi+1) invariant sets in such n-neuron neural networks can be guaranteed by applying the Brouwer’s fixed-point theorem as well as upper and lower functions method. Furthermore, it is demonstrated that these equilibrium points and invariant sets remain valid for the same neural networks when subjected to an appropriate controller. Then, on the basis of Lyapunov function method and impulsive control theory, two finite-time multistability theorems are established for Hopfield neural networks under distinct impulse scenarios: stabilizing impulses and destabilizing impulses. The settling time estimations for determining the local finite-time stability of Πni=1(Mi+1) equilibrium points are developed by designing general impulsive sequences, which reveal that the settling time is dependent on initial state, impulsive effects and control parameters. From the perspective of impulsive effects, the introduced stabilizing impulses in neural networks not only accelerate the convergence rate but also yield tighter upper bound of settling time estimation relative to impulse-free systems. In stark contrast, destabilizing impulses significantly degrade the convergence performance while resulting in more conservative upper bound of settling time estimation. Finally, theoretical results are shown to be effective by two illustrative examples and two associative memory applications of grayscale image.

Multi-Behavior recommendation captures fine-grained user intents by jointly modeling multiple interaction behaviors, significantly improving recommendation accuracy and diversity. However, existing methods often overlook complex semantic relations between items and entities. Moreover, constructing separate subgraphs for different interaction types frequently results in sparse graph structures, which exacerbates the long-tail problem. To address these challenges, we propose Long-tail Inductive Feature Transfer (LIFT), a method for knowledge-aware multi-behavior recommendation. To mitigate uneven node distribution in graph structures, we introduce a knowledge transfer-based feature reconstruction mechanism. Specifically, we first drop a portion of the neighbors of head nodes to construct proxy representations for tail nodes, training a reconstructor on the tail proxy to reconstruct the original head node features. The trained reconstructor is then used to backfill missing neighbor information for tail nodes, thereby achieving a more balanced feature distribution across nodes. Furthermore, we integrate multi-behavior and semantic contrastive learning to jointly optimize the representations. Extensive experiments on four datasets demonstrate that LIFT outperforms state-of-the-art methods, with further analysis validating its uniformity in representation learning. The source code is available at:https://github.com/city59/LIFT.

Contrastive learning (CL) has demonstrated exceptional capability in extracting supervised signals and mitigating noise, increasingly attracting interest for its application in knowledge graph-enhanced (KG) recommendation. Nevertheless, existing approaches predominantly rely on a single augmentation strategy to construct contrastive views, often failing to effectively balance feature perturbation with semantic preservation. To overcome this limitation, we propose motif-guided multiview contrastive learning (MMCL), a unified framework for contrastive augmentation. MMCL leverages diverse view generation strategies across the user–item bipartite graph and the KG, perturbing graph structures while preserving essential semantics to the greatest extent possible. Specifically, for collaborative signals, we devise a data augmentation mechanism to model the interaction dynamics between users and items. For semantic information, we introduce a novel graph augmentation technique by constructing an interactive motif KG, enabling semantic contrastive learning within localized views. Furthermore, joint enhancement of graph and interaction data allows the model to capture global topological features of nodes. MMCL integrates contrastive learning at both local and global levels, effectively embedding local semantics and global topology into the learned representations. Extensive experiments conducted on three publicly available datasets reveal that MMCL outperforms advanced methods, particularly in scenarios with sparse interactions and noisy KG, while significantly alleviating popularity bias in recommendation.

RGB-thermal salient object detection (RGB-T SOD) aims to identify prominent objects by integrating complementary information from RGB and thermal modalities. However, learning the precise boundaries and complete objects remains challenging due to the intrinsic insufficient feature fusion and the extrinsic limitations of data scarcity. In this paper, we propose a novel hybrid prompt-driven segment anything model (HyPSAM), which leverages the zero-shot generalization capabilities of the segment anything model (SAM) for RGB-T SOD. Specifically, we first propose a dynamic fusion network (DFNet) that generates high-quality initial saliency maps as visual prompts. DFNet employs dynamic convolution and multi-branch decoding to facilitate adaptive cross-modality interaction, overcoming the limitations of fixed-parameter kernels and enhancing multi-modal feature representation. Moreover, we propose a plug-and-play refinement network (P2RNet) which serves as a general optimization strategy to guide SAM in refining saliency maps by using hybrid prompts. The text prompt ensures reliable modality input, while the mask and box prompts enable precise salient object localization. Extensive experiments on three public datasets demonstrate that our method achieves state-of-the-art performance. Notably, HyPSAM has remarkable versatility, seamlessly integrating with different RGB-T SOD methods to achieve significant performance gains, thereby highlighting the potential of prompt engineering in this field. The code and results of our method are available at: https://github.com/milotic233/HyPSAM.

Multi-modal object tracking leverages the complementary attributes of different modalities, such as visible+ thermal (RGBT) or visible+depth (RGBD) data, to enhance tracking performance. However RGBT or RGBD image can not obtain reliable object features for limitations derived from dense or low-illumination scenarios, respectively. This work explores improving tracking reliability by integrating the advantages and complementarity of visible, depth, and thermal (RGBDT) data. To achieve this goal, a triple-modal object tracking dataset (VDT1k) and a triple-modal object tracking network (TOTrack) are proposed in this work. VDT1k includes 1,325 RGBDT image sequences (totaling 240k RGBDT image pairs) that cover 12 common scenarios of downtown streets. Meanwhile, 7 challenging attributes are annotated with binary labels to evaluate the detailed performance of the tested methods under these extreme scenarios. Considering the specific attributes of different modalities, TOTrack adopts asymmetric feature extraction and multi-stage feature fusion to fuse RGBDT information and complete the object tracking task. The asymmetric feature extraction treats the depth modality as a distance perception information that divides the visible and thermal images into three parts (object, background, and foreground). The multi-stage feature fusion employs different fusion strategies for the early, middle, and late fusion of visible and thermal modalities. Experimental results show that TOTrack outperforms current RGBD/RGBT methods on the test data of VDT1k. TOTrack demonstrates that integrating the complementary information of RGBDT data is a more effective approach to further improve tracking reliability and performance. Realted sources can be accessed at the link.

In this article, we address the error estimation problem of quasi-synchronization for a class of multilayer dynamical networks. The proposed network model simultaneously accounts for interlayer and intralayer time-varying coupling structures, network directionality, and interlayer communication delays. To achieve synchronization in a cost-effective manner, we design a novel pinning impulsive control strategy that leverages large-scale impulse delay information together with the number of pinned nodes. By employing an iterative algorithm, we establish a new delay-dependent impulsive differential inequality, which precisely characterizes the convergence domain and provides flexibility in the choice of impulse delays. Then, some quasi-synchronization criteria are derived to guarantee convergence of multilayer networks within a prescribed error level, and explicit analytical expressions for the synchronization error bounds are obtained. Finally, to demonstrate the practical applicability, the proposed criteria are applied to the synchronization of multilayer single-link robot arm networks under error bounds, with numerical examples validating the effectiveness of the method.

This article investigates quasi-projective synchronization (Q-PS) of discrete-time fractional-order delayed memristive neural networks (DFDMNNs) with uncertainties. Firstly, by virtue of some useful inequality skills and basic properties of discrete-time fractional calculus as well as fixed-point theorem, several sufficient criteria on the existence of solutions for DFDMNNs with uncertainties are derived. Furthermore, Q-PS of DFDMNNs is explored under the delayed state feedback controller, and corresponding Q-PS criteria are established. Finally, one numerical example is presented to verify the availability of the theoretical results.

For the first time, this article presents a dissipativity-based observer design for accurate state-of-charge (SOC) estimation, essential for improving the safety, performance, and lifespan of lithium-ion batteries (LIBs) in electric vehicle (EV) battery management systems (BMSs). However, model uncertainties and measurement noise significantly affect estimation accuracy. To address this, a novel observer design based on ( $\mathcal {Q}, \mathcal {S}, \mathcal {R}$ )- $\gamma $ -dissipativity theory is developed, formulated within a linear matrix inequality (LMI) framework, and integrated with the Lyapunov-Krasovskii functional (LKF) approach. The proposed observer ensures robustness and stability in SOC estimation under uncertain and noisy conditions. A one-resistor capacitor (1-RC) equivalent circuit model (ECM) is adopted for battery modeling, with experimental validation performed on a Panasonic 18650PF cell. The proposed method is compared against the adaptive unscented Kalman filter (AUKF) under four drive cycles: the urban dynamometer driving schedule (UDDS), the aggressive US06 supplemental federal test procedure, the Los Angeles 92 (LA92), and the highway fuel economy test (HWFET). Results show that the proposed observer achieves root-mean-square errors (RMSEs) of 0.77%, 0.50%, 0.65%, and 0.48% and mean absolute errors (MAEs) of 0.59%, 0.42%, 0.50%, and 0.40% under UDDS, US06, LA92, and HWFET, respectively. This corresponds to RMSE reductions of 28.38%, 88.93%, 67.25%, and 38.35% compared with AUKF. Notably, the proposed method achieves a maximum accuracy of 99.23%, surpassing the latest reported accuracy of 98.50%.

This work addresses the nonfragile fault-tolerant control for power cyber-physical systems (CPSs) under denial-of-service (DoS) attacks, in which the cyber attacks are considered to be strongly concealed. In the fact of power CPSs frequently subject to various attacks during the operation, the innovation is to construct a double-layer stochastic process composed of a semi-Markov chain and a sequence of observed mode to analyze the DoS attacks from the viewpoint of hidden semi-Markov chain. To address potential actuator failures and controller gain perturbations, an observed-mode-dependent nonfragile control scheme is developed. By constructing a mode-dependent Lyapunov function that incorporates both attack modes and observed modes, sufficient conditions are derived to ensure mean-square stability of the closed-loop system through the semi-Markov kernel (SMK) approach, which systematically handles the stochastic characteristics of dwell time (DT) distributions under incomplete attack mode information. Finally, a simulation example demonstrates the validity of the proposed approach.

This article investigates the prespecified performance consensus problem for a class of nonlinear multiagent systems (MASs) with unknown actuator faults. By employing a sensor-triggered mechanism and neural estimation algorithm, a novel leader-follower consensus protocol is devised for the nonlinear MASs. The developed sensor event-triggered mechanism comprises two parts, the first one is sensor event-triggered sampling, and the second one is event-triggered information transmission. Due to the presence of the sensor-triggered mechanism, the system states cannot be available in real time. In order to solve this challenge, a signal decomposition and compensation strategy is constructed to balance the intermittent sensor-sampled signals and the real system inputs. Furthermore, the considered actuator faults in each follower are not limited to be finite, the time, frequency and mode of the faults are also unknown. To address the unknown actuator faults in the nonlinear MASs, a resilient fault management mechanism is developed for each follower. Based on the managed actuator faults dynamics, some bounded estimation signals are constructed and the issue of "explosion of complexity" in the backstepping design procedure is eliminated through the application of nonlinear filters with compensation terms. Finally, simulation results are given to illustrate the effectiveness of developed control protocol.

Note that the available barrier Lyapunov function (BLF) design considers that the precondition of the specified function must be a smooth convex function, which is relatively harsh for most models. In this article, built on proportional-integral (PI) theory, an adaptive neural network (ANN) PI iterative learning tracking control method for fractional-order nonlinear systems (FONSs) with full-state constraints is presented. A new type of BLF is built that only requires finding a derivative of this function needs to be monotonic under the fractional Lyapunov direct method. To meet the needs of reducing computational complexity and data volume, the designed backstepping controller based on PI control consists of a series of constant gains and dynamic variables with basic linkage relationships. Moreover, it also incorporates iterative learning algorithm that can achieve continuous or discontinuous self-learning and updating. The results indicate that all closed-loop signals of FONSs are semi-globally ultimately uniformly bounded and the constraint is not violated. Theoretical analysis and numerical simulation have verified the rationality of this study.

This paper explores the sliding mode synchronization issue in a fractional-order quaternion-valued Takagi-Sugeno fuzzy neural network model with parametric uncertainty using a non-decompositional approach. An integral sliding surface is developed, and a sliding mode controller is designed based on the principles of sliding mode control. To ensure synchronization, we construct a Lyapunov function and apply fractional-order theory, quaternion theory, and inequality analysis to establish a criterion that is sufficient to ensure that the systems achieve synchronization via sliding mode control. Furthermore, we perform a reachability analysis on the sliding surface. Finally, we verify our conclusions through numerical examples and apply our research findings to image encryption and decryption. Simulation results demonstrate that the research method presented in this paper effectively enhances security and improves encryption efficiency in color image encryption, while maintaining high image restoration quality.

Traditional consensus control for multiagent systems (MASs) often suffers from excessive communication burden due to continuous control input updates. This paper presents a sampled-data event-triggered (SDET) adaptive fuzzy bipartite consensus control scheme for fractional-order MASs. A fuzzy state observer is constructed to estimate unmeasurable states and unknown nonlinearities. An SDET condition integrates event errors, fuzzy weight estimates, and bipartite consensus errors. Distinct from existing input-variation-based triggering mechanisms, the proposed SDET mechanism updates and transmits the control and adaptive laws only at aperiodic sampled-data instants, significantly reducing computational and communication load. To address the nonsmooth and piecewise constant virtual control signals induced by the SDET mechanism, a fractional-order command filter is incorporated into the backstepping design, effectively eliminating the explosion of complexity and facilitating rigorous stability analysis. Within a hybrid-system framework, the boundedness of all closed-loop signals is rigorously proven, and a strictly positive lower bound on inter-event intervals is derived to exclude Zeno behavior. Simulation results on a fractional-order single-machine infinite-bus power system demonstrate the effectiveness and superiority of the proposed scheme.

This paper proposed a model-free adaptive security control (MFASC) method designed for nonlinear vehicle systems, simultaneously tackling controller saturation, external disturbances, and aperiodic Denial-of-Service (DoS) attacks. To achieve a desired platoon control objective with only input-output data, the dynamics are reconstructed into a differential form with pseudo partial derivative parameters. Under saturation constraints, a de-saturation factor is introduced to ensure the controller’s state remains within permissible limits. Additionally, a disturbance observer is included to estimate external disturbances and mitigate their negative effects. For aperiodic DoS attacks, an attack compensation mechanism is developed. Comprehensive stability analysis demonstrates the ultimate boundedness of the formation error of vehicle systems under aperiodic DoS attacks. The validity of the theoretical results is supported by numerical simulations.

Visual object tracking (VOT) is a fundamental task in computer vision that underpins many higher-level tasks. Previous VOT methods have explored various approaches to improve the performance of VOT tasks and achieve predetermined goals. However, due to the limitations of a single view, issues such as visual blind spots and glitches in photographic devices present insurmountable challenges, often causing the visual features of the tracked object to vanish entirely or partially. Providing supplementary images from another view is an effective way to improve tracking performance by eliminating blind spots and enhancing the reliability of image sources. In this work, we propose a novel benchmark called the Aerial and Ground Cooperation Dataset for VOT (AGVOT). AGVOT consists of 643 sequences from aerial and ground views, comprising 119,859 image pairs. These image pairs were collected from 15 scenes and span 26 object categories. Every sequence is strictly time-aligned and annotated with 4-point bounding boxes. For comprehensive evaluation, AGVOT is further categorized into 12 challenging attributes, each labeled at the frame level. Moreover, we introduce an Aerial and Ground Cooperation Tracking Network (AGTrack) to demonstrate dual-view feature integration and cross-view tracking cooperation. Extensive experiments show that AGTrack outperforms 10 state-of-the-art (SOTA) methods in the computer vision community when tested on AGVOT’s dataset and its challenging attributes. These results demonstrate that implementing VOT tasks with cooperative views achieves more accurate and reliable tracking performance. The project source is available at the link.

This work addresses the dissipative synchronization for complex dynamical networks (CDNs) with semi-Markov jump parameters under adaptive event-triggering. Due to the complexity of network environment, a semi-Markov process (SMP) is introduced into the system modeling. The adaptive event-triggering protocol (AETP) is developed to dynamically adjust the triggering threshold based on the synchronization error, which significantly reduces unnecessary data transmissions and avoids Zeno behavior by guaranteeing a positive inter-event interval. Subsequently, a nonfragile controller is designed for potential fluctuations in controller gain. By constructing Lyapunov–Krasovskii functional (LKF) and employing advanced integral inequalities, sufficient synchronization conditions are derived to ensure that the CDNs achieve stochastic stability with strictly dissipative characteristic. Simulation results demonstrate the effectiveness of the proposed control scheme.

This article introduces a learning-based model predictive control (MPC) framework that leverages Gaussian mixture models (GMMs) to address dynamic system uncertainties effectively. To address the limitations of traditional MPC methods in handling system disturbances and uncertainties, we integrate GMM into the MPC framework to model and account for these disturbances probabilistically. By reformulating the chance constraints within this framework, the proposed approach provides high-probability safety guarantees. Specifically, GMM can capture complex disturbance distributions in dynamic systems compared with traditional Gaussian processes, thereby enabling more precise prediction and optimization within the MPC loop. This ensures that the resulting control strategies not only satisfy safety constraints but also enhance system performance. Experimental evaluations demonstrate that the proposed method achieves superior performance in satisfying high-probability safety requirements while enhancing the overall robustness of the system. Compared to conventional MPC approaches, the proposed method can effectively tackle the challenges posed by uncertainties, ensuring stability and safety under diverse conditions. This approach has broad applications in domains that require robust control under uncertainties, including autonomous driving, robotic manipulation, and other complex engineering systems.

Geographically distributed data centers (DCs) offer significant spatiotemporal flexibility and are ideal candidates for demand response in power systems. This paper proposes a two-stage robust spatiotemporal optimal scheduling model for geographically distributed DCs that accounts for the uncertainty in both renewable energy generation and workload. In the first stage, a day-ahead scheduling approach is employed based on predicted workload and renewable energy generation values to determine pre-scheduling strategies. In the second stage, between the day-ahead and intraday periods, an iterative algorithm is introduced to calculate the number of reserved servers, thereby mitigating the workload overload issues arising from workload uncertainty. Power fluctuations resulting from the uncertainty of the predicted values are balanced using energy storage and generators. Finally, simulation results validate the effectiveness of the proposed algorithm in reducing overcapacity and enhancing the economic efficiency of the scheduling model.

The cooperative-competitive network has broad application prospects due to its advantages in aligning with practical production and life. This work, for the first time, combines the predictive capability of the proportional-integral-derivative (PID) protocol for error and the flexibility of an additional order parameter in fractional calculus to solve the distributed resource allocation problem in cooperative-competitive networks, and provides a discrete time algorithm for the design. The analysis proves that the algorithms can Mittag-Leffler converge to the optimal solution of the distributed resource allocation problem. Finally, numerical simulations are provided, demonstrating the effectiveness of the algorithm, along with a set of comparative simulation that validate the superiority of the proposed algorithm.

This paper proposes a novel optimization algorithm named PIDNAG, which innovatively incorporates the PID control strategy-comprising proportional, and time-dependent integral, and derivative components-into the Nesterov accelerated gradient method for solving convex optimization problems. From a control-theoretic perspective, we conduct rigorous theoretical analysis to demonstrate that the proposed method not only guarantees convergence but also significantly accelerates the optimization process. Extensive experimental results show that PIDNAG achieves remarkable convergence performance in various practical learning tasks, solidly validating its superior capability in convex optimization problems.

Prescribed-time stable systems frequently suffer from infinite gain issues that lead to practical infeasibility. This paper investigates the problem of stabilization for uncertain Euler-Lagrangian systems and proposes a singularity-free prescribed-time stabilization controller. Based on the time space deformation approach, the designed controller ensures singularity avoidance, effectively eliminating the existence of infinite gain. Besides, the considered Euler-Lagrangian systems are subject to unknown nonlinear functions and derivative-bounded external disturbances with unknown bounds. To achieve system stabilization under such uncertainty issue, this paper formulates multiple sliding manifolds with prescribed-time stability. The controller’s effectiveness is validated through simulation studies on a rendezvous formation problem.

Microbes are microorganisms with biological molecules and have significant therapeutic potential for treating diseases, underscoring the need for computational methods to screen microbes targeting disease-associated drugs. However, the computational methods often consider node embedding or structure features between microbes and drugs, and have a severe class imbalance problem inherent in sparse association data. In this work, we proposed a heterogeneous graph collaborative representation learning model that combines the merits of attentive fusion and reciprocal distillation for drug-related microbe prediction. First, we constructed the heterogeneous biological information and meta-path-induced graphs of microbes and drugs. Then, a topological structure feature encoder is devised to extract complex topological and semantic interaction patterns from heterogeneous biological graphs with microbes and drugs, while an efficient transformer concurrently extracts discriminative semantic and structural information based on the graph position information of nodes. Next, a reciprocal distillation schema is developed to mitigate the adverse effects of the data imbalance problem, and enable the distribution consistency of the model between topological and semantic information extraction. Moreover, we devised a dual collaborative feature fusion schema that combines graph topological and dual meta-path-based semantic features to obtain the discriminative features of microbes and drugs. Through reciprocal distillation, an efficient optimization function focuses on hard-to-classify samples of drug-related microbes via discriminative features. Extensive experiments demonstrate that our model could deal with the association sparsity problem and extract more semantics and structure. Meanwhile, case studies indicate that our model could discover reliable candidate microbes associated with a special drug.

This paper delves into the state estimation and finite-time (FT) synchronization for fractional neural networks with time delayed. Firstly, a Luenberger observer is proposed to estimate the unknown state. Secondly, the controller with sign or saturation functions is designed to achieve FT synchronization, which the saturation functions is utilized to avoid chattering phenomenon. Correspondingly, several algebraic-form-based synchronization criteria and the setting times are presented by means of the Jensen inequality, fractional-order Razumikhin theorem and fractional-order calculus property. Finally, two examples are listed to illustrate the correctness of the adopted approaches.

Medical image fusion aims to integrate multi-sensor source images into a unified representation, providing comprehensive and diagnostically enriched information to support clinical decision-making. However, the scarcity of labeled data presents significant challenges in effectively learning complementary features across modalities. In this paper, we propose a novel attribute-aware contrastive learning network, called ACL-Net, boosting medical image fusion performance. Specifically, we introduce the attribute transformation strategy to simulate variations in pixel intensity and structural patterns, guiding the model to focus on critical cross-modal information. In this way, it enhances contrastive learning by generating diverse negative pairs, thereby mitigating the scarcity of negative samples in unsupervised fusion scenarios. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art medical image fusion methods.

This article delves into quasi-projective synchronization (Q-PS) problem for a class of discrete-time fractional-order T-S fuzzy complex-valued neural networks (DFTSFCNNs) with leakage and time-varying delays. First of all, according to the theory of discrete fractional calculus and properties of Mittag-Leffler function, an innovative property of discrete Mittag-Leffler function is strictly proved, and then an inequality for dealing with mixed time delays is rigorously derived. Next, by utilizing Caputo fractional difference theory and combining with property and the inequality offered in this article, some easily verifiable Q-PS criteria are derived under complex-valued fuzzy linear controller. Eventually, a numerical example is presented to demonstrate availability of the derived results.

The problem of predefined-time (PdT) synchronization of FitzHugh-Nagumo (FHN) neural networks is investigated. The FHN neural networks have shown proficiency in accurately representing the essential characteristics of neurons and effectively simulating the structural dynamics and functioning of the brain. The investigation of synchronization in FHN neural networks will enhance our comprehension of how neurons behave dynamically and promote their valuable utilization in the field of artificial intelligence. In this study, the successful integration of adaptive control and PdT control techniques not only achieves synchronization within a predetermined time but also eliminates the need to preselect controlling gains. The work develops new PdT stability theorems for singular perturbation systems and synchronizing controllers that exclude linear terms. Consequently, a set of sufficient conditions for achieving this synchronization goal is provided. Furthermore, it mathematically demonstrates the implementability of an upper bound for the settling time by parameterizing the predefined time as a controller parameter, thereby eliminating the need for explicit recalculations. Moreover, the control gains can be adjusted adaptively, enhancing the flexibility of control parameter tuning. Finally, several numerical simulations are presented to validate the effectiveness of the designed adaptive PdT control strategies. Note to Practitioners—The study of neural network-based synchronization in nonlinear systems is rapidly emerging as an important tool in many fields, including automation, telecommunications, computer science, mathematics, physics, and biology. Among the neural network architectures, FitzHugh-Nagumo (FHN) neural networks stand out for their ability to closely model the behaviors and dynamics of real neurons. This capability is not just theoretical; it has significant practical applications in enhancing the understanding of brain functions and improving the performance of artificial intelligence systems. In particular, neural networks with different time scales have better self-learning and generalization ability. Our innovative approach is the integration of adaptive control with predefined-time control techniques. This combination not only guarantees synchronization within a specific time frame but also dynamically adjusts these control parameters, enhancing the system’s adaptability to changes and disturbances without requiring prior knowledge of specific control gains. In practical applications, achieving efficient synchronization can significantly enhance AI systems, making them more efficient and reliable. For instance, in autonomous vehicles, better synchronization can improve decision-making processes, while in robotics, it can lead to more advanced and responsive automations.

Using the backstepping technique, this paper formulates innovative adaptive finite-time stabilizing controllers for uncertain nonlinear systems featuring nonuniform input quantization and asymmetric, time-varying output constraints. These novel controllers leverage the consistent characteristics of both hysteresis quantizers and logarithmic quantizers. Quantization errors, when consistent, become unbounded and contingent on control input, rendering them incompatible with the growth conditions of nonlinear systems. Consequently, the developed adaptive controllers eliminate the reliance on growth conditions, effectively addressing the impact of unbounded quantization errors on finite-time stability. This adaptability allows the controllers to function effectively with systems employing either hysteresis quantizers or logarithmic quantizers. The paper establishes the convergence of these controllers through the finite-time Lyapunov stability theorem. It also provides a comprehensive guideline for tuning settling time, enabling fine-grained control over finite-time convergence and adjustable tracking error performance. Additionally, the controllers rigorously maintain system output within predefined limits. Their effectiveness and low computational burden are demonstrated through three comparative numerical simulations and a practical simulation in collision-free trajectory tracking control of an autonomous vehicle platoon using the vehicle motion software CarSim. These simulations confirm the advanced performance of the adaptive controllers.Note to Practitioners—This paper introduces an innovative approach to control uncertain nonlinear systems encountering intricate input quantization and output constraints. Employing the sophisticated backstepping technique, the authors present adaptive finite-time-stabilizing controllers engineered to address nonuniform input quantization and asymmetric, time-varying output restrictions. What distinguishes these controllers is their reliance on the consistent behavior exhibited by hysteresis and logarithmic quantizers. This unique feature equips them to effectively counteract unbounded quantization errors influenced by control input. Most notably, these controllers eliminate the conventional growth conditions typically demanded by nonlinear systems. As a result, they extend their applicability to a broad spectrum of systems employing either hysteresis or logarithmic quantizers. The research also provides practitioners with a valuable guideline for precisely adjusting settling time. This enables the attainment of desired convergence rates while permitting adaptable tracking error performance. Additionally, these controllers guarantee that the system’s output adheres to predefined limits. The practical significance of this study is highlighted through three comparative numerical simulations and a real-world application simulation. This real-world simulation involves collision-free trajectory tracking control of an autonomous vehicle platoon, executed using the vehicle motion software CarSim. These simulations unequivocally demonstrate the effectiveness and low computational burden of the developed controllers, thereby establishing them as a valuable resource for practitioners facing complex control challenges in various domains.

This paper investigates the optimal control problem for interconnected systems with unknown system dynamics through a two-stage reinforcement learning method. First, to address the impact of interconnection term, the optimal control problem for interconnected systems is transformed into obtaining the solution of game algebraic Riccati equations within the framework of$H_{\infty }$control method. Furthermore, existing optimal control approaches for interconnected systems necessitate precise knowledge of system dynamics, which is difficult to obtain accurately or involves high costs. Thus, we introduce a two-stage reinforcement learning method. The admissible control policies are obtained using the homotopy-based iteration method in the first stage. Then, the optimal control policies are obtained through the policy iteration method in the second stage. The two-stage method not only eliminates the requirement for system dynamics and initial admissible control policies but also ensures convergence speed and accuracy, significantly enhancing its practicality. Finally, a two-machine power system example is provided to validate the feasibility of the two-stage method. Note to Practitioners—Interconnected systems, a class of systems composed of multiple local subsystems, find wide applications in various fields such as power systems, transportation networks, and spatially interconnected systems. Particularly, the optimal control problem of interconnected systems has gradually become a focal point of current research. However, the current research on the optimal control problem of interconnected systems is still constrained by the system dynamics and the initial stability of the system. To relax these limitations, this paper introduces a two-stage method. A homotopy-based iteration approach is employed to obtain control policy and interconnection policy that make the system closed-loop stable, thus achieving the optimal solution. Furthermore, the data-driven approach overcomes the limitations imposed by system dynamics. The feasibility of the two-stage method is illustrated by a two-machine power system model.

In this paper, we explore the stabilization of Boolean Networks (BNs) under state-flipped control, incorporating three key constraints to address the challenges of disease treatment response times and the practical limitations of therapeutic interventions. Our approach includes: (i) a feasible flipped nodes constraint to ensure a subset of nodes can be realistically manipulated; (ii) a transient period constraint to align treatment with time-sensitive windows of therapeutic opportunity; and (iii) a constraint on the number of flip transitions to minimize resource consumption and potential side effects. Furthermore, an evolution selection rule is set for the BNs. Then, a necessary and sufficient condition is given for the stabilization under the above constraints. We present a modified Q-learning (QL) algorithm to design a policy for stabilization under constraints in the model-free case. Finally, BN models of metastatic melanoma network and T-cell large granular lymphocyte (T-LGL) network are considered to show the applicability of our results.

This paper investigates, for the first time, the non-fragile synchronization of dynamic memristor-delayed neural networks (DM-DNNs) under the influence of actuator saturation and stochastic cyber-attacks. To minimize communication overhead, a novel memory-driven event-triggered mechanism (METM) is introduced, incorporating features such as switching operational modes, utilization of historical data, and a dynamically adjustable threshold parameter. Meanwhile, a nonfragile controller is proposed to deal with the uncertainty in complex control environments where actuator saturation and cyber-attacks occur. Furthermore, a less conservative loop functional, whose positive definiteness in rest mode is no longer required, is constructed innovatively. Then, some new sufficient criteria for synchronization and its codesign algorithm for controller gain are given successively. Finally, numerical examples are presented to validate the synchronization outcomes obtained in this paper.

This paper endeavors to address the output-feedback bipartite consensus issue for uncertain high-order multi-agent systems (MASs) through the utilization of a dynamic event-triggered control methodology. To achieve this goal, a novel output-feedback distributed control strategy is developed for each follower agent. This strategy utilizes a bounded time-varying gain along with a predefined-time compensator. This combined approach facilitates the achievement of bipartite consensus within a specified timeframe. The key highlight of this methodology lies in the introduction of a newly devised time-varying gain, which not only enhances the convergence rate but also significantly improves transient performance. Additionally, a new dynamic event-triggered mechanism is developed for each agent, leveraging the newly introduced predefined-time internal dynamic variable. This mechanism ensures the achievement of predefined-time bipartite consensus while significantly reducing communication frequency. Comparative simulations are conducted to validate the superiority of the obtained results. Note to Practitioners—For real physical agent systems, it is impractical to gather all the state information. Hence, there is a critical need to investigate output feedback consensus algorithms that eliminate reliance on state information. However, existing output feedback consensus algorithms require the continuous transmission of control instructions to actuators, resulting in high communication costs and transmission burden. Additionally, in practical requirements, designers are eager for multiple agents to achieve the required stable performance at a predefined time rather than infinite time. For this reason, this paper presents a new output-feedback event-triggered control methodology to achieve a predefined-time bipartite consensus of uncertain nonlinear MASs. To improve the system’s convergence speed and transient performance, we introduce a novel time-varying gain. Next, we devise a dynamic event-triggering control for each agent utilizing the newly introduced predefined-time internal dynamic variable. This approach effectively reduces communication frequency while ensuring predefined-time bipartite consensus.

In this article, we contribute to dynamic event-triggered-based leader-follower quantized consensus design for fractional-order (FO) nonlinear multi-agent systems (MASs) under asymmetric time-varying state constraints. Firstly, we propose a novel dynamic event-triggered mechanism (ETM) to fully utilize the channel resources between the controller and actuator. Subsequently, by means of the characteristics of quantization nonlinearities and the architecture of FO nonlinear MASs, the influence of quantized inputs is eliminated. In the consensus protocol design procedure, the radial basis function neural networks (RBF-NNs) technology is employed to dynamically estimate the uncertain functions existing in the system. Then, by employing a bivariate FO derivative lemma and some convex time-varying barrier Lyapunov functions (BLFs) with two variables, a dynamic event-triggered-based leader-follower quantized consensus protocol is constructed. Exhaustive theoretical analysis manifests that the tracking errors of the FO nonlinear MASs converge to a small region, all signals in the FO nonlinear MASs are bounded, and the asymmetric time-varying state constraints can be achieved regardless of the presence of the quantized inputs and dynamic event-triggered communication. Finally, a chaotic Duffing FO nonlinear MASs is employed to demonstrate the efficacy of the developed consensus protocol.

This work proposes a variant of Colored Traveling Salesman Problem (CTSP) called Capacitated Colored-traveling-salesman Problem with Time-windows (CCPT), which comes from time-sensitive logistics applications. CCPT is first formulated via mathematical programming formulations, and an Elite-guided Memetic Algorithm (EMA) is developed to tackle it. EMA is able to preserve an active archive of elites during an evolution process. It comprises three schemes, i.e., neighborhood-list-2-opt, relocation move, and cross-arc exchange. They are organized in a variable neighborhood descent framework to optimize a specific high-quality individual. Ablation studies fully show their importance for EMA’s performance. 28 CCPT cases are designed based on representative traveling salesman problem instances to conduct benchmark tests. The statistical comparison shows that EMA is significantly better than Variable Neighborhood Search (VNS), Delaunay-triangulation-based VNS, local search, and memetic algorithm in over 85% of the cases. It achieves faster convergence to the solutions than its competitors.Note to Practitioners—This work is motivated by practical needs for distribution logistics with time-sensitive requirements. A capacitated colored-traveling-salesman problem with time-windows is modeled and can be applied to time-sensitive transportation tasks with multiple goods. An elite-guided memetic algorithm is developed to tackle this problem. It executes the local optimization on a specific high-quality solution during the search process. Extensive comparisons demonstrate that the proposed algorithm can provide decision-makers with significantly better routes than other state-of-the-art algorithms.

This study considers the finite-time prescribed performanceH∞tracking consensus control problem in nonlinear multi-agent systems with time-varying disturbance and designs a distributed event-triggered control strategy. First, a class of time-dependent error performance constraint functions is introduced to effectively address the negative impact of disturbances on the transient and steady-state performance of a system. Second, a dynamic event-triggered protocol defined using periodic sampling data is proposed to reduce the information transmission between agents and decrease the controller’s update frequency. Third, the designed dynamic event-triggered mechanism is combined with the finite-timeH∞control theory, and sufficient conditions for the system to achieve tracking consensus in a finite time are obtained by employing the Lyapunov stability theory and linear matrix inequality. Finally, the feasibility of the proposed distributed control strategy is verified through numerical simulations and practical examples.

This paper focuses on the problem of outlier-resistant distributed filtering under dynamic event-triggered scheme (DETS) and cyber-attacks over sensor networks. A novel distributed DETS is proposed, which allows us to minimize the data-releasing rate while simultaneously preventing abrupt data from being sent over the network. Consider the situation of measurement outliers in presence, a saturation function is used in distributed filter design to confine the contaminated signals so as to improve filtering performance. The occurrence of stochastic DoS attacks which may corrupt the measurement in the sensor networks are also considered. Sufficient conditions for the expected filter are derived while guaranteeing the asymptotically stability of the filtering error system with a guaranteed$H_{\infty}$performance index$\gamma$. A numerical simulation is given to illustrate the effectiveness of the theoretical analysis and design method.Note to Practitioners—The issue of filtering over sensor networks is subject to network impections induced by the limited communication resources and cyber-attacks. Since these imperfections may lead to performance degration or instability of the system, it is critical to take some measures to reduce unnecessary signal communication among sensor nodes and eliminate the adverse effects of cyber-attacks. Besides, the measurement outliers are frequently encountered due to sensor aging or abnormal disturbances, which may impair the accuracy of the estimation resuts. In this paper, the outlier-resistant distributed filtering problem is investigated in sensor networks subject to dynamic event-triggered schemes and DoS attacks. A co-design method of the DETS and the distributed filter is propsed which can gurantee the desired system performance.

This paper focuses on the finite-time projective synchronization (FTPS) of fractional fuzzy inertial delayed neural networks (FFIDNNs) with parameters perturbation via several quantified event-triggered mechanisms (QETMs). First, applying Laplace transform, a linear fractional finite-time inequality is established for the infinite time interval rather than a finite time interval. Next, three kinds of QETMs are designed, which can respond flexibly to practical challenges. By the established inequalities and the designed controller, some novel criteria are derived to guarantee FTPS for the considered system. Moreover, the settling time is estimated and the event interval has a positive lower bound to avoid the Zeno behavior. Finally, the accuracy of theoretical findings is confirmed via numerical examples.

This study develops the space-time sampled-data control problem for memristor-based reaction-diffusion neural networks (MRDNNs) using a memory event-triggering scheme. Unlike traditional Markov switching models with fixed transition probabilities, the nonhomogeneous sojourn probabilities are capable of describing the switching behavior more accurately. By effectively leveraging historical transmitted packets, an innovative mode-dependent memory event-triggering scheme that incorporates nonhomogeneous sojourn probability information is introduced, optimizing communication resource utilization. A space-time sampled-data control law is designed by sampling both spatial and temporal domains, significantly reducing network communication resource consumption while achieving the desired system performance. The validity and superiority of the proposed space-time sampled-data control strategy are demonstrated through a simulation example.

This paper proposes a novel prescribed performance control scheme for stochastic micro-electro-mechanical system (MEMS) gyroscopes, addressing three critical issues overlooked by existing methods: control torque oscillation during rapid convergence, deviation in steady-state tracking errors in a global asymmetric design, and violation of monotonic constraints due to insufficient input capability. To tackle these challenges, the paper proposes a quadratic prescribed performance function design, a local asymmetric constraint design, and a customized non-monotonic design. These innovations effectively resolve the technical difficulties and establish comprehensive performance specifications for stochastic MEMS gyroscopes. The proposed scheme ensures boundedness in probability for all closed-loop signals and convergence of the tracking error to an arbitrarily small residual within a prescribed time. Simulation results confirm the effectiveness and superiority of the scheme.

Einstein Würfelt Nicht(EWN) is a stochastic game involving random information and uncertainty. Due to inherent randomness, many algorithms struggle to effectively solve EWN, and their level of play requires enhancement. In this article, a novel belief state-based upper confidence bound for trees (BS-UCT) algorithm is introduced for the EWN game. The algorithm addresses the challenges posed by random information in EWN by maintaining a set of belief states to estimate the impact of dice rolls, effectively improving the efficiency of Monte Carlo Tree Search under uncertainty. We also propose a self-playing method of warm-start reinforcement learning with an improved value network structure, using a pretrained model to generate high-quality data and incorporating a coordinate attention mechanism to improve feature processing. Finally, an efficient lock-free parallel search algorithm is presented, enabling rooted parallel tree search and significantly improving the search efficiency of BS-UCT during simulation. The experimental results show that the BS-UCT algorithm has an advantage over other baseline methods, with 89%, 70%, 74%, and 64% win rates for random, beta pruning, normal UCT, and quick neural network tree search (QNNTS) algorithm, respectively, highlighting its effectiveness in the processing of random information and achieving high performance in EWN.

This article proposes a novel framework of nonhomogeneous sojourn probabilities in the 2-D domain and investigates the observer-based security control issue for 2-D fuzzy switched systems (SS) under an adaptive-threshold-based saturation function. In comparison with the existing homogeneous/nonhomogeneous Markov processes, the proposed nonhomogeneous sojourn probability framework enhances measurability and reduces computational complexity, making it a more reasonable approach. The security control challenge for 2-D fuzzy SS is addressed in the context of randomly occurring attacks with uncertain occurrence rates, where the uncertainty is norm-bounded and asymmetrical around the nominal rate. To mitigate the effects of these abnormal attacks, a novel adaptive-threshold-based saturation function is employed, ensuring that transmitted information remains within an acceptable range. Leveraging Lyapunov theories, sufficient conditions are attained to guarantee the asymptotically mean square stable with noise attenuation performance. The validity of the proposed theoretical results is demonstrated through a simulation example.

This article is concerned with a distributed algorithm for seeking the Nash equilibrium in noncooperative games with partial-decision information, which simultaneously addresses the protection of individual privacy and ensures fast algorithmic convergence. First, a differential privacy mechanism is used in the fully distributed consensus-based projected pseudo-gradient algorithm to obfuscate shared messages over the communication network and quantify the algorithm's privacy level. To achieve fast convergence, a novel relaxed inertial method is designed, consisting of two steps with independently designed parameters: 1) a relaxation step and 2) an inertia step. The adaptive inertia coefficient in the inertia step is designed based on the iteration error of the players' estimated decisions and a decaying sequence, with the only requirement being the non-negativity of its internal parameters. Compared to existing approaches, our algorithm exhibits high flexibility in parameter selection. Furthermore, we analyze the algorithm's convergence and differential privacy under both linearly decaying and fixed stepsizes within a unified framework, providing sufficient conditions that are independent of the number of players. Finally, numerical simulations validate the algorithm's potential, demonstrating significant improvements in convergence rate, accuracy, and privacy level.

This article aims to address the fixed-time optimal leader-following consensus issue for unknown multiagent systems (MASs) under Denial of Service (DoS) and false data injection (FDI) attacks. A novel fixed-time stability theorem under DoS attacks is presented to simplify the stability conditions and decrease the computational complexity of the settling time. Simultaneously, the deep neural networks (DNNs) structure with the projection operator are adopted in real-time to approximate the unknown system dynamics. To achieve the optimal consensus under cyber-attacks, a hierarchical control approach is presented, which includes a reference signal generation layer and a tracking control layer. Specifically, the distributed and Luenberger-based observers are designed in the reference signal generation layer to solve the fixed-time state estimation issues of leader and followers under multiple malicious attacks, respectively. Then, the optimal control strategy based on the event-triggered mechanism (ETM) is designed in the tracking control layer to track the reference signal and minimize the cost consumption. Due to the difficulty in obtaining explicit expressions of the optimal control mechanisms, a critic-only reinforcement learning (RL)-based algorithm is presented for online learning the unknown weight within a fixed time. By rigorous proof, the developed observers can achieve the fixed-time state reconstruction and the optimal control policy can track observation states after a fixed time. Finally, simulation results about platooning control of automated vehicles are given to demonstrate the efficacy of the developed technique.

The principal innovative contribution of this study resides in the introduction of a category of fractional delayed large-scale neural networks characterized by intricate topological structures. Additionally, this article provides a comprehensive exploration of novel outcomes linked to fractional order-induced bifurcations in large-scale networks. In the initial step, the correlation of the artificial neural network and the graphical neural network is established through the Mason's diagram method. Subsequently, the system's characteristic equations are derived by employing the Coates' flow graph decomposition method. Moving on, through the concept of the global element, an exhaustive investigation delves into the distribution of eigenroots. The sum of synaptic transmission delays among neurons is considered as a bifurcation parameter, with an analysis focused on the stability of the trivial equilibrium and the existence of the Hopf bifurcation. Following this, the optimal fractional order-dependent stability interval is determined using the implicit function array curve method, presenting a novel approach for critical value determination. Finally, the drawn conclusions are substantiated through multiple sets of computer simulations. It is indicated that an increase in delay precipitates the onset of Hopf bifurcation. Moreover, a reduction in the fractional order significantly improves the steady-state performance of the system. However, once the fractional order value descends below the left stability boundary, the system's stability is compromised, leading to the emergence of periodic oscillations. The prediction algorithm proposed in this article offers valuable insights into selecting the appropriate fractional order for large-scale complex networks.

This article studies the adaptive fuzzy asynchronous (AFA) stabilization of discrete networked hidden stochastic semi-Markovian switching power systems under cyber attacks. Due to the complex network environment, cyber-attacks are taken into account, in which the fuzzy logic rule is adopted to describe the unknown deception attacks. Considering the mismatch mechanism between the controller and the system, an adaptive fuzzy controller runs asynchronously with the system, where the hidden semi-Markovian model is used to characterize the asynchronous mechanism. Based on the detected mode and the fuzzy logic rule, an AFA stabilizing controller is designed for the underlying system. Using the stochastic Lyapunov function related to the detected mode and system mode, sufficient criteria are given for the AFA controller design, ensuring that the underlying system is bounded stable in the mean square. Finally, the proposed scheme is verified by the simulated example.

A dynamic event-triggered load frequency control (LFC) is studied for interconnected multiarea power systems (IMAPSs) with stochastic semi-Markov parameters from the perspective of finite-time interval. To facilitate the sudden changes, the underlying semi-Markov process (SMP) is adopted to characterize the random behavior of IMAPSs. A dynamic event-triggered protocol (DETP) is developed to modulate the transmission frequency while maintaining predefined system performance. Owing to complicated grid environment, the hidden semi-Markov model (HSMM) is proposed to solve the asynchronization between the system mode and the controller mode, which forms a new asynchronous mechanism to better understand the behavior pattern of the system. The novelty of this article is to construct a suitable asynchronous control strategy to solve the mismatch between the system mode and the controller mode under the framework of IMAPSs. Different from static event-triggered protocol (ETP), the DETP is proposed, in which the threshold parameters can be dynamically adjusted to reduce the waste of communication resources and achieve dynamic performance in limited time. According to the stochastic system theory and the finite-time theory, by constructing a modular dependent random Lyapunov function, sufficient conditions are obtained to ensure the finite-time boundedness of the corresponding system with performance. Finally, the efficiency is demonstrated through three-area power systems.

This study investigates the synchronization of reaction-diffusion complex dynamical networks (CDNs) based on semi-Markov switching topology and an event-triggered protocol. The investigated model is rendered more practical via the introduction of a semi-Markov process for stochastic jump CDNs. Based on the internal dynamic variable history information, a dynamic-memory event-triggered strategy is proposed, wherein the primary novelty lies in its prior transmitted packets to enhance the control performance. This further reduces data transmission based on the dynamic threshold parameters. The Bessel-Legendre inequality is adopted to reduce the conservatism of the obtained results. In addition, sufficient synchronization conditions are established to ensure the stochastic stability of the error system for two different models (partial differential equations-and ordinary differential equations-based models). Furthermore, two examples are provided to illustrate the effectiveness of the theoretical results.

This work studies the novel sliding mode control (SMC) of discrete nonlinear stochastic switching models under semi-Markovian parameter and incomplete semi-Markovian kernel (SMK). The characteristic of nonlinear system is described by an interval type-2 fuzzy (IT2F) model that can be recognized as a collection of several type-1 fuzzy models. The uncertainties in system parameters is efficiently captured using the lower and upper grades of membership. Based on the mode-dependent Lyapunov function and incomplete SMK, sufficient conditions are proposed to ensure the stability of sliding dynamics. Moreover, an IT2F SMC law based on learning strategy is developed such that the state signals are guided onto the predetermined sliding region and the mode switchings-induced chattering is effectively reduced. Finally, the IT2F SMC strategy is validated through the simulation of truck-trailer model.

This article provides a new impulsive observation approach [called impulsive adaptive observer, impulsive adaptive observation (IAO)] for a class of linear systems. A discrete-time-based adaptive rule for the impulsive observer gain is designed using only output information at discrete-time intervals (or impulsive instants), overcoming the real-time data requirement of continuous-time adaptive observation frameworks. The IAO effectively estimates the states of a continuous-time system and demonstrates outstanding state tracking performance in practical implementations. Furthermore, the IAO-based feedback controller is designed to stabilize the controlled plant. Stability criteria for the IAO protocols are established, showing improved performance over existing schemes by reducing computational load and enhancing control flexibility. The simulations for the electrical system are presented finally to confirm the effectiveness of the IAO and its control approach.

The bipartite consensus (BC) issue for nonlinear multiagent systems (NMASs) with unknown system dynamics information is investigated in this article. Initially, the dynamics of NMASs are represented using the Takagi-Sugeno (T-S) fuzzy model. Subsequently, to achieve distributed control, a minmax game policy is introduced, where each agent aims to minimize its performance index while its neighbors attempt to maximize it. Consequently, the BC problem for NMASs is reformulated as a zero-sum game, transforming the controller design into solving a set of game algebraic Riccati equations (GAREs). To solve such equations, a novel scaling off-policy iteration (PI) algorithm is proposed. The key features of the proposed learning algorithm can be outlined in three main aspects: 1) during the learning process, the reliance on system dynamics is relaxed; 2) compared with the PI method, the requirement for initial admissible control policies is eliminated; and 3) a more rapid convergence speed is achieved than traditional value iteration. Finally, the effectiveness and advantages of the proposed method are validated through a simulation example and a series of comparative experiments.

This article presents an adaptive neural network control scheme with prescribed performance for stochastic nonlinear systems. Unlike existing adaptive stochastic control schemes that primarily utilize deterministic neural networks for approximations in complex stochastic environments, we employ stochastic neural networks to approximate the stochastic nonlinear terms, effectively resolving the "memory overflow" issue. Moreover, we propose a novel prescribed performance design method, which distinguishes itself from the previous prescribed performance control schemes by integrating a quadratic characteristic capable of suppressing transient input vibrations, along with a local asymmetric characteristic that optimize both transient output overshoot and steady-state error bias. Furthermore, the proposed control scheme is implemented within a fixed-time framework to ensure that all closed-loop systems are fixed-time bounded in probability, with the tracking error consistently within the predefined performance bounds. Simulation results validate the effectiveness of the proposed control scheme.

This article aims to probe synchronization problem of discrete-time fractional-order T-S fuzzy complex-valued neural networks (DFTSFCNNs) with time delays and uncertainties. First, three important power-law inequalities regarding Caputo fractional$\theta$-difference are strictly attested. Next, a fuzzy$m$-norm Lyapunov function (FMLF) that relies on membership functions is designed to replace traditional Lyapunov functions and obtain synchronization criteria. Then, a unique complex-valued fuzzy nonlinear delayed feedback controller is devised, and by virtue of the FMLF method and newly derived inequalities herein, several sufficient criteria are derived to ensure complete synchronization of DFTSFCNNs. Lastly, the validity of the main results is demonstrated by numerical simulations, and an application of the obtained results in image encryption is also provided.

This study explores the protocol-based sampled-data control for T-S fuzzy reaction–diffusion neural networks (RDNNs) with nonhomogeneous sojourn probabilities (NSPs). By incorporating a deterministic switching signal, a new framework of NSPs is developed to characterize the random behaviors of fuzzy RDNNs. Unlike previous studies, this work introduces multiasynchronous switching among fuzzy RDNNs, triggering conditions, and controllers using dynamic asynchrony models, effectively capturing mode transitions through NSPs and detection probabilities. An improved adaptive event-triggered protocol is created by integrating fuzzy rules and detection probability information. Moving beyond traditional time-domain sampled-data control strategies, a space–time sampled-data control approach is proposed to significantly reduce communication load. Benefiting from Lyapunov theory, criteria are attained for ensuring the mean-square exponential stability of the systems under consideration. Ultimately, the proposed space–time sampled-data control strategy is validated through a simulation example, highlighting its effectiveness and superiority.

This article addresses the fixed-time event-triggered impulsive secure synchronization issue of a category of Takagi–Sugeno quaternion-valued fuzzy neural networks (QVFNNs) under stochastic cyber-attacks for the first time. To do this, dynamic event-triggered control, fuzzy rules, and impulsive control are combined to create a well-designed event-triggered impulsive control scheme that can significantly decline the consumption cost and communication burden. Meanwhile, in virtue of two different and independent Bernoulli random variables, the stochastic cyber-attack model involving not only denial-of-service attacks but also deception attacks is introduced. Then, some algebraic criteria are deduced to accomplish fixed-time synchronization for the QVFNNs with or without stochastic cyber-attacks through the fuzzy set theory, Lyapunov functional method, and impulsive system theory. Furthermore, the nonexistence of the Zeno phenomenon can be guaranteed. Ultimately, an illustrative instance is revealed to manifest the correctness of the theoretical outcomes and the practicability of the current strategy.

In this article, we tackle the problem of analyzing dissipativity, via devising switching mechanisms, for switched positive Takagi–Sugeno (T–S) fuzzy systems with time-varying delay. To leverage the positivity properties of state, input, and output variables, a novel concept of dissipativity is developed, focusing on linear supply rates and linear copositive storage functionals. When state information is available, the state dependent switching mechanism satisfying a dwell time constraint is introduced dependent on the constructed time-varying multiple linear copositive storage functionals. This mechanism allows for solving the dissipativity issue for the entire system without imposing any solvability requirements on subsystems and reduces the switching frequency. In cases, where state information is unavailable, dissipativity is ensured by a dwell-time dependent switching mechanism. Further, all conditions guaranteeing the solvability of the problem are presented in the form of linear vector inequalities. Two simulation examples are finally offered, demonstrating that the proposed techniques are effective and superior.

This article focuses on the leader–following consensus in fixed time for multiagent systems under denial-of-service (DoS) attacks. First, the node-based distributed DoS attack model is developed, where multiple adversaries launch different attack strategies working together on nodes of the system. Second, a novel fixed-time stability theorem is presented, where an index function is introduced to decrease the complexity of the stability condition and achieve the stability goal under node-based distributed DoS attacks. Third, based on this index function, the resilient dynamic event-triggered mechanism (DETM) is designed by utilizing the DoS attacks detection signals to reduce the consumption of computational costs and save the system resources. With the assistance of Lyapunov stability theory, some consensus conditions are established and the Zeno behavior is excluded. Finally, the autonomous swarm systems, including autonomous aerial vehicles and autonomous ground vehicles, are applied to illustrate the effectiveness of control strategy.

Accurate pathological image segmentation is crucial for the clinical diagnosis of breast cancer. However, existing methods of pathological segmentation face challenges due to the variability and complexity of breast cancer on pathological images. To address these issues, we propose a novel segmentaion network called template-matching pathological segmentation network. Our method incorporates an innovative template matching strategy inspired by the diagnostic process of pathologists. The template matching strategy is to utilize visual transformer to establish a correlative relationship between cancer lesions and corresponding templates. To improve feature utilization of pathological images, PSVTNet introduces detailed information attention and information entropy attention. Detailed information attention aims to exploit detailed information by serving as the path connecting shallow-layer and deep-layer features. Meanwhile, information entropy attention can redistribute feature weights to high-entropy regions according to the information-entropy attention map. Additionally, this work releases a comprehensive pathological dataset that comprises labeled pathological images. These images are collected from breast and stomach cancers with hematoxylin&eosin and human epidermal growth factor receptor-2 staining. Extensive experiments demonstrate that PSVTNet significantly outperforms state-of-the-art methods on pathologic images of breast cancer, but can also process pathologic images of stomach cancer carrying with same diagnosed features as the breast cancer.

Navigating restricted waterways is widely considered one of the most stressful phases for ship operators due to the confined navigable areas and the uncertainty of encounter situations. Most existing ship collision avoidance methods are developed for open-water navigation and do not adequately address the unique constraints and uncertainties associated with restricted environments. This paper proposes PROMPTER, a probabilistic motion planning framework designed to assist pilots in navigating restricted waterways. PROMPTER transforms motion planning into an inference task by integrating ship maneuverability, situational awareness, and path planning within a Bayesian factor graph, allowing optimal policies to be derived through posterior inference over control. Within the graph, operational restrictions are encoded as prior knowledge to constrain and guide the control inference process. Furthermore, we theoretically prove the closed-form solution and convergence of the constrained control inference. To ensure that PROMPTER is applicable in diverse operational conditions, we consider scenarios with both reliable and unreliable communication, leading to the development of cooperative and non-cooperative collision avoidance strategies. Experiments conducted in both synthetic and real-world restricted waterways demonstrate that PROMPTER outperforms existing methods by generating collision-free and kinematically feasible paths.

In intelligent transportation systems (ITS), machine learning is highly effective in anomaly detection for state estimation (SE) in traffic flow measurement, but it requires resource-intensive collection of anomaly samples and overlooks spatial characteristics. Additionally, the original SE in traffic flow measurement exhibits low-frequency characteristics due to computational complexity. Considering these limitations, this paper proposes a fast and efficient anomaly detection framework for SE in traffic flow measurement. Initially, an attention-based spatiotemporal graph convolutional network (ASTGCN) model is utilized to extract both temporal and spatial features of SE. The ASTGCN model is trained using quantile regression, relying solely on normal samples during training. This approach eliminates the need for collecting anomalous samples and reduces computational demands. The model establishes a safe interval for SE that significantly improves anomaly detection capabilities. Furthermore, a teacher-student network is implemented, where the teacher network, trained offline, distills its ability to convert low-frequency SE data into high-frequency representations into a simpler student network. The student network executes real-time reconstruction of high-frequency SE, thus enhancing the precision of anomaly detection without substantial resource expenditure. Finally, the analysis conducted on the California’s 7th district highway measurement system demonstrates the proposed method’s ability to accurately detect anomalous SE in traffic flow measurement.

With its well-designed network architecture, the deep learning-based infrared and visible image fusion (IVIF) method shows its efficiency and effectiveness by realizing a fine feature extraction and fusion mechanism. However, disparities in cross-modal features often result in an imbalance between texture details and contextual information, causing detailed features to be overshadowed by prevailing contextual information. To tackle this issue, this study introduces PIDFusion, a fusion model driven by a PID controller, designed to dynamically optimize cross-modal feature fusion deviations. The core of PIDFusion is the dynamic adaptation capability of the PID controller, which facilitates real-time corrections for deviations encountered during the fusion process, thereby maintaining a harmonious balance between texture details and contextual information. Additionally, we introduced the Cyclic Self-Supervised Feature Refinement (CSSFR), which under the constraint of self-supervised loss, minimizes redundant information within the feature flow and ensures the preservation of salient feature through the cyclic input of decoupled features. Concurrently, we developed the Iterative Attention Module (IAM), utilizing the unique gating mechanism of LSTM to capture feature changes across successive iterations, thereby driving the model to cultivate more discriminative feature representations. Extensive experiments revealed that PIDFusion outperforms SOTA methods in terms of both efficiency and cost-effectiveness, through static statistics and high-level vision tasks. Our code is available athttps://github.com/wang-x-1997/PIDFusion.

The multistability and its application in associative memories are investigated in this article for state-dependent switched fractional-order Hopfield neural networks (FOHNNs) with Mexican-hat activation function (AF). Based on the Brouwer's fixed point theorem, the contraction mapping principle and the theory of fractional-order differential equations, some sufficient conditions are established to ensure the existence, exact existence and local stability of multiple equilibrium points (EPs) in the sense of Filippov, in which the positively invariant sets are also estimated. In particular, the analysis concerning the existence and stability of EPs is quite different from those in the literature because the considered system involves both fractional-order derivative and state-dependent switching. It should be pointed out that, compared with the results in the literature, the total number of EPs and stable EPs increases from and to and , respectively, where with being the system dimension. Besides, a new method is designed to realize associative memories for grayscale and color images by introducing a deviation vector, which, in comparison with the existing works, not only improves the utilization efficiency of EPs, but also reduces the system dimension and computational burden. Finally, the effectiveness of the theoretical results is illustrated by four numerical simulations.

In recent years, the analysis of the dynamics of annular neural networks has received extensive attention and achieved some achievements. However, most of the current research merely focuses on the single-ring, low-dimension, two rings sharing one neuron cases, without considering the rich coupling modes between rings. In this article, a large-scale time-delay fractional-order dual-loop neural network model with cross-coupling structure is established, in which two rings complete information interaction through two shared neurons. Moreover, the Caputo fractional derivative is introduced in this article to describe the neural network more accurately. First, the transmission time delay between each neuron is selected as the key parameter leading to the bifurcation, and the characteristic equation of the network is creatively derived using the Coates flow graph method. Subsequently, through the holistic element method and magnitude angle formula, we simplify the analytical process. Then, we obtain the stability and Hopf bifurcation criterion of the network. Finally, the conclusions of the theoretical analysis are verified by a series of numerical simulations. The results show that the stability region of the network is closely related to the fractional order, the number of neurons, the distribution of neurons, and the self-feedback coefficients. Moreover, the time delays have a significant effect on the amplitude and period of the Hopf bifurcation.

This article is committed to studying projective synchronization and complete synchronization (CS) issues for one kind of discrete-time variable-order fractional neural networks (DVFNNs) with time-varying delays. First, two new variable-order fractional (VF) inequalities are built by relying on nabla Laplace transform and some properties of Mittag-Leffler function, which are extensions of constant-order fractional (CF) inequalities. Moreover, the VF Halanay inequality in discrete-time sense is strictly proved. Subsequently, some sufficient projective synchronization and CS criteria are derived by virtue of VF inequalities and hybrid controllers. Finally, we exploit numerical simulation examples to verify the validity of the derived results, and a practical application of the obtained results in image encryption is also discussed.

Infrared and visible image fusion (IVIF) aims to obtain an image that contains complementary information about the source images. However, it is challenging to define complementary information between source images in the lack of ground truth and without borrowing prior knowledge. Therefore, we propose a semisupervised transfer learning-based method for IVIF, termed STFuse, which aims to transfer knowledge from an informative source domain to a target domain, thus breaking the above limitations. The critical aspect of our method is to borrow supervised knowledge from the multifocus image fusion (MFIF) task and to filter out task-specific attribute knowledge by using a guidance loss , which motivates its cross-task use in IVIF tasks. Using this cross-task knowledge effectively alleviates the limitation of the lack of ground truth on fusion performance, and the complementary expression ability under the constraint of supervised knowledge is more instructive than prior knowledge. Moreover, we designed a cross-feature enhancement module (CEM) that utilizes self-attention and mutual-attention features to guide each branch to refine features and then facilitate the integration of cross-modal complementary features. Extensive experiments demonstrate that our method has good advantages in terms of visual quality and statistical metrics, as well as the docking of high-level vision tasks, compared with other state-of-the-art methods.

Circadian rhythm disruptions are linked to increased cancer risk and unfavorable prognosis in patients with cancer, highlighting the critical role of the interplay between the circadian rhythm factor Per2 and the tumor suppressor p53. This brief presents, for the first time, a mathematical model to capture the dynamics of the p53-Per2 network in DNA-damaged cells. The model accurately describes the different stages of the process from unstressed cells to cellular repair and finally to apoptosis as the degree of DNA damage increases. Furthermore, it is found that increasing the inhibition of Per2 by p53 leads to the phase advance of Per2 oscillations, whereas by modulating the inhibition of Mdm2 by Per2, an independent amplitude modulation of active p53 can be achieved, with the range of modulation increasing with the strength of the inhibition. Moreover, the effects of time delays inherent in the transcription, translation, and nuclear translocation of Per2 on the circadian rhythm of DNA-damaged cells are quantitatively investigated by theoretical analyses. It is found that time delays can induce stable oscillations through a supercritical Hopf bifurcation, thereby maintaining the circadian function of DNA-damaged cells and enhancing their DNA-damage repair capacity. This study proposes new insights into cancer prevention and treatment strategies.

Activation functions have a significant effect on the dynamics of neural networks (NNs). This study proposes new nonmonotonic wave-type activation functions and examines the complete stability of delayed recurrent NNs (DRNNs) with these activation functions. Using the geometrical properties of the wave-type activation function and subsequent iteration scheme, sufficient conditions are provided to ensure that a DRNN with n neurons has exactly $(2m + 3)^{n}$ equilibria, where $(m + 2)^{n}$ equilibria are locally exponentially stable, the remainder $(2m + 3)^{n} - (m + 2)^{n}$ equilibria are unstable, and a positive integer m is related to wave-type activation functions. Furthermore, the DRNN with the proposed activation function is completely stable. Compared with the previous literature, the total number of equilibria and the stable equilibria significantly increase, thereby enhancing the memory storage capacity of DRNN. Finally, several examples are presented to demonstrate our proposed results.

The exponential asynchronous stabilization (EAS) issue for a category of neural networks (NNs) with semi-Markov jump (SMJ) parameters and additive time-varying delays (ATDs) is addressed in this article. Here, the SMJ parameters in the controller gain are supposed to be distinct from those in the system structure, which is more consistent with the actual situation. To further relieve the communication load of the network, a new discrete adaptive event-triggered impulsive control (DAEIC) scheme is proposed, where the impulsive moments are the sampling instants satisfying event-triggered constraints, and the triggering threshold can be dynamically adjusted by an adaptive update rule (AUR) related to the current sampling state and the last triggered state. A more flexible looped Lyapunov-Krasovski functional (LLKF) is constructed to commendably capture the available information about impulsive instants, triggering state, sampling interval, ATDs, and heterogeneous SMJ parameters. Combined with the LLKF, DAEIC scheme, and other inequality analysis approaches, some novel results guaranteeing the EAS of the underlying systems are exported. Finally, three explanatory examples are presented to check the validity of our results.

Facing large-scale online learning, the reliance on sophisticated model architectures often leads to nonconvex distributed optimization, which is more challenging than convex problems. Online recruited workers, such as mobile phone, laptop, and desktop computers, often have narrower uplink bandwidths than downlink. In this article, we propose two communication-efficient nonconvex federated learning algorithms with error feedback 2021 (EF21) and lazily aggregated gradient (LAG) for adapting uplink and downlink communications. EF21 is a new and theoretically better EF, which consistently and substantially outperforms vanilla EF in practice. LAG is a gradient filtration technique for adapting communication. For reducing communication costs of uplink, we design an effective LAG rule and then give EF21 with LAG (EF-LAG) algorithm, which combines EF21 and our LAG rule. We also present a bidirectional EF-LAG (BiEF-LAG) algorithm for reducing uplink and downlink communication costs. Theoretically, our proposed algorithms enjoy the same fast convergence rate as gradient descent (GD) for smooth nonconvex learning. That is, our algorithms greatly reduce communication costs without sacrificing the quality of learning. Numerical experiments on both synthetic data and deep learning benchmarks show significant empirical superiority of our algorithms in communication.

This article investigates a distributed fixed-time secondary control (FTSC) scheme to eliminate dc voltage deviations caused by voltage-droop control (VDC) in a multiterminal dc transmission system (MTDCTS) with switching topology and achieve precise power sharing within a fixed time frame. The distributed FTSC combines a dc voltage controller and a power sharing controller. The main objective is to restore the average dc voltage of the converters to dc voltage reference of MTDCTS within a fixed time. Additionally, it also enables power sharing among the converters based on their individual capacities. Compared to conventional distributed consensus control (DCC), fixed-time control (FTC) presented in this article exhibits a shorter convergence time and is unaffected by the initial state of MTDCTS. In this article, each converter communicates exclusively with its adjacent converters via a communication network that may undergo changes over time, which helps alleviate the strain on the communication network. To test the FTSC, a five-terminal MTDCTS with two wind farm converters (WFCs) is created in power systems computer aided design (PSCAD)/electromagnetic transients including DC (EMTDC).

This article is dedicated to an investigation of state estimation for discrete-time fractional-order nonautonomous neural networks (DFNNNs) with leakage and discrete delays. To this end, some inequalities with more free parameters are obtained based on results related to nabla fractional difference, which considerably extend the existing results. In light of the effective estimator, some sufficient conditions to ensure the global asymptotic stability of the error system are obtained to solve the state estimation problem for DFNNNs by means of the linear matrix inequality (LMI) and the established inequalities. Finally, the theoretical results are verified by numerical simulations.

This article aims to address the finite-time consensus issue for variable-order fractional multiagent systems (MASs) under denial of serve (DoS) attacks. First, the framework of node-based DoS attack is introduced, including its influences, detection, and recovery mechanisms. Second, a new variable-order fractional difference (VO-FD) inequality is proposed to accurately evaluate the variable-order fractional derivative of the power function. Based on the developed inequality, two novel finite-time stability theorems are presented based on the developed inequality, which fill up the gap in variable-order fractional finite-time stability without and with DoS attacks, respectively. Third, a resilient distributed control protocol is proposed, where the dynamic event-triggered mechanism (DETM) is introduced based on the detected signal of attack to ensure the desired control performance and economize system resources. According to the Lyapunov stability theory, the consensus can be achieved within the settling time, and some linear matrix inequality (LMI)-based conditions are obtained. Finally, the feasibility and validity of theoretical analysis is illustrated by simulation example.

This study aims to analyze the network coherence of a class of polygonal networks under noise distribution. First, we derive the exact analytical solutions of first-order and second-order leaderless network coherence of a class of polygonal networks. Second, we investigate the impact of network topology on network coherence. The network coherence and robustness of the first-order leaderless networks are significantly improved with increasing the number of iterations. Finally, we demonstrate an approximately linear relationship between the second-order network coherence and the number of nodes in polygonal networks. These findings offer valuable insights into the dynamic properties and optimization strategies for polygonal networks.

This work explores the issue of multistability for a competitive neural network (NN) class with sigmoidal activation functions (AFs) involving state-dependent switching and fractional-order derivative. Specifically, first, we consider three different switching point locations, and establish some sufficient criteria ensuring that NNs with$n$-neurons can have, and only have,$5^{n_{1}}\cdot 3^{n_{2}}$equilibrium points (EPs) with$n_{1}+n_{2}=n$, by utilizing the geometric features of the sigmoidal functions, the fixed point theorem, the Filippov’s EP definition, and the contraction mapping theorem. Then, based on novel Lyapunov functions and by applying the fractional-order calculus theory, it is demonstrated that$3^{n_{1}}\cdot 2^{n_{2}}$out of$5^{n_{1}}\cdot 3^{n_{2}}$total EPs are locally stable. This work’s investigation reveals that competitive NNs with switching afford more storage capacity compared to the nonswitching case. Additionally, our results are valid for the integer-order and fractional-order switched NNs, improving and generalizing current works. Furthermore, two numerical examples and an application example of associative memory are provided to validate the effectiveness of the theoretical findings, and the way various fractional orders affect the NNs’ convergence speed is shown through simulations.

This article considers the stability of nabla linear time invariant (LTI) fractional order systems with the order$\alpha \in (0,+\infty)$. First, the stable criterion is developed, by using the nabla Laplace transform. Compared with the existing case of$\alpha \in (0,1)$, our work introduces a wide range of dynamic behaviors for future applications. Second, many essential properties are discussed for the developed criterion, including the changing trend of the stable/unstable region regarding the order, the containment relationship between the imaginary axis, the negative semi-axis and the stable region, the evolution of the modulus with the absolute value of argument for the point lying in the critical stable region. Third, the linear matrix inequality (LMI) condition is tentatively derived to evaluate the stability. Finally, the elaborated results are supported by three illustrative numerical examples.

In this article, the optimal control for a nonlinear affine system under aperiodic Denial-of-service (DoS) attacks is investigated. To solve the Hamilton–Jacobi–Bellman (HJB) equation, an adaptive dynamic programming (ADP) algorithm based on a single critic network is developed. The proposed regulative learning rate strategy outperforms traditional fixed-rate gradient descent approaches found in existing works. With the objective of minimizing the performance index, the optimal value function and the optimal controller are derived from the approximate solution of the HJB equation. To alleviate the resource demand and enhance the flexibility of the threshold function, an adaptive event-triggered (AET) scheme integrating the idea of sampling control and event-triggered strategy is applied to the optimal control initially. Compared with the static event-triggered strategy, the AET method contains increasing engineering value. A piecewise Lyapunov function is constructed based on optimal value function, estimated error introduced by neural network (NN) weight, and the classic Lyapunov-Krasovskii function. Thus, uniform ultimate boundedness for tracking error is proven theoretically. Moreover, the maximum tolerable strength of cyberattacks is provided from the stability analysis. The simulation results exhibit the designed approach’s reliability.

Due to the complex entanglement between distributed control and distributed estimation, adaptive multiagent dynamics over leaderless directed graphs are yet not completely understood. This happens even when the adaptive dynamics are based on established tools like model reference adaptive control (MRAC). This work starts from the observation that existing MRAC-based leaderless designs stop at asymptotic consensus, lacking of any guarantee for exponential consensus or parameter identification. The main contribution of this work is to show a new design departing from the existing ones in terms of exploiting persistence of excitation (PE): the proposed design is the first one attaining exponential consensus with exponential parameter identification over leaderless directed graphs. In the special case that the unknown parameters are homogeneous, PE can be relaxed to a weaker cooperative PE (C-PE) condition. The design is illustrated and verified alongside the state-of-the-art.

In the multimodal (text, audio, and visual) sentiment analysis, the current methods generally consider the bi-modal sentiment interaction, resulting in inadequate mining and fusion of relations between modalities. In this paper, we propose the concept of multimodal 3D (3-Dimensional) stereoscopic attention for the first time, which constructs the tri-modal stereoscopic attention with temporal sequences simultaneously to adequately structure the sentiment interaction. To solve the problems of stereoscopic attention construction such as the increased complexity of algorithms caused by rising dimensions, we propose a progressive construction method with 2D attention as an intermediate process. To implement sentiment relations based on stereoscopic attention to integrating modal information sufficiently, a forward propagation mechanism is proposed, which optimizes the representations of each modality with multimodal modulation. The results on two public datasets confirm the superiority of the proposed method in all metrics to the baselines.

In this paper, we propose an unsupervised learning approach for the task of infrared and visible image fusion. This approach is called a distinctive-feature guided and hierarchical channel enhanced network-based infrared and visible image Fusion (DFG-HCEN). Instead of using complex fusion rules, DFG-HCEN uses multi-level fusion to achieve fusion results, effectively avoiding information loss during feature extraction. To improve the fusion effect, we designed a distinctive-feature guided module that strengthens the relationship between modules. Moreover, the proposed hierarchical channel enhanced and distinctive-Feature guided module aims to facilitate the fusion framework in efficiently integrating the multilevel complementary features of the source pictures. In addition, we incorporate a hybrid loss method for unsupervised training of the provided DFG-HCEN. The fidelity loss is used to constrain the pixel similarity between the fused result and source images. The application of luminance regularization loss has been shown to be an efficient method for addressing the problem of luminance degradation in fused images. We conducted extensive experiments, including visual examination and quantitative analysis , comparing DFG-HCEN with thirteen other state-of-the-art fusion techniques. The results demonstrate the superiority of DFG-HCEN. Moreover, the extended object detection experiments validate the ability of DFG-HCEN to fully support downstream tasks.

This article mainly centers on proposing new fixed-time (FXT) stability lemmas of discontinuous systems, in which novel optimization approaches are utilized and more relaxed conditions are required. The conventional discussions about V t > 1 and 0 < V t ⩽ 1 are no longer required. For the purpose of verifying the new lemmas, complex-valued fuzzy cellular neural networks (CVFCNNs) with discontinuous activation functions are studied with a nontraditional non-separation method, which could reduce the conservatism of the obtained results greatly. Besides, FXT synchronization is discussed simultaneously. When contrasted with the results of other similar prominent pioneering works nowadays, the accuracy of settling times (STs) is quite enhanced. At last, numerical simulations are conducted to demonstrate the validity and superiority of our established theoretical results.

Abstract In this article, the issue on projective synchronization of delayed inertial quaternion-valued neural networks (IQVNNs) is investigated. Different from most existing literature, we adopt the non-reduced order approach to deal with IQVNNs described by second order differential equations. By introducing a novel Lyapunov functional, several sufficient criteria are presented in component form to ensure the projective synchronization between master–slave systems. A numerical experiment demonstrates the feasibility of control strategy as well as the correctness of theoretical results.

Abstract In this paper, a two-neuron reaction–diffusion neural network with discrete and distributed delays is proposed, and the state feedback control strategy is adopted to achieve control of its spatiotemporal dynamical behaviours. Adding two virtual neurons, the original system is transformed into a neural network only containing the discrete delay. The conditions under which Hopf bifurcation and Turing instability arise are determined through analysis of the characteristic equation. Additionally, the amplitude equations are derived with the aid of weakly nonlinear analysis, and the selection of the Turing patterns is determined. The simulation results demonstrate that the state feedback controller can delay the onset of Hopf bifurcation and suppress the generation of Turing patterns.

Abstract This paper investigates the topology identification and synchronization in finite time for fractional singularly perturbed complex networks (FSPCNs). Firstly, a convergence principle is developed for continuously differential functions. Secondly, a dynamic event-triggered mechanism (DETM) is designed to achieve the network synchronization, and a topology observer is developed to identify the network topology. Thirdly, under the designed DETM, by constructing a Lyapunov functional and applying the inequality analysis technique, the topology identification and synchronization condition in finite time is established in the forms of the matrix inequality. In addition, it is proved that the Zeno behavior can be effectively excluded. Finally, the effectiveness of the main results is verified by an application example.

Optimal energy management of a power system enables the economic operation of grids. However, traditional centralized methodologies face limitations when dealing with large-scale power systems. To overcome these challenges, an optimal energy management model is developed for networked microgrids (NMGs), and a distributed neurodynamic optimization algorithm is proposed for the optimization model. Before facilitating power exchange between neighboring microgrids (MGs), a strict priority scheme for power distribution is outlined. Distributed power generation takes precedence in meeting load requirements, followed by charging and discharging operations. Furthermore, to ensure the stability of the overall grid, power transmission is established between neighboring MGs. To solve the optimization problem, a distributed recurrent neural network is developed, and it is demonstrated to converge towards an optimal solution. An illustrative example involving 3-MG NMGs is elaborated to demonstrate the validity and effectiveness of the proposed algorithm.

This paper investigates the$L_{\infty }$-gain of incommensurate fractional-order delayed positive systems (FODPSs), in which a mixture of unbounded delays and distributed delays is considered. Through the utilization of the Banach’s fixed point theorem, the solution to the system is shown to exist uniquely. Then, two necessary and sufficient criteria achieving the positivity and stability of FODPSs with mixed delays are proposed, respectively. For the purpose of calculating the$L_{\infty }$-gain, a sample data system is formulated to approximate the lower bound of system trajectories. Additionally, it reveals that the duration of distributed delays have an impact on the$L_{\infty }$-gain, whereas unbounded delays will not. Finally, the validity of the theoretical results is explanted through a numerical simulation.

The development of integrated energy system has promoted the development of the power system into the largest and most complex cyber-physical energy system, where the change in the openness of the load frequency control (LFC) system has also increased its susceptibility to false data injection attacks (FDIAs). This paper porposes an artificial intelligence (AI) based detection and countermeasure strategy to protect LFC systems from FDIAs. First, The levenberg-marquarelt-back propagation (LM-BP) neural networks (NNs) is trained by collecting the historical data of frequency deviation, power deviation of contact line, and active power load deviation. Second, the output control signal of LM-BP NNs is tested against the output of the system controller for residuals to determine whether has FDIAs. Furthermore, to resist the negative effects of FDIAs, the control signal calculated by the LM-BP NNs will replace the traditional proportion integration differentiation (PID) output. Finally, The advantages of the proposed strategy are illustrated by the two interconnected power systems.

Optimal control has become a hot topic due to its ability to reduce control costs. However, due to the complex form of fractional-order (FO) derivatives, it is difficult to obtain the optimal control solution by solving the FO Hamilton-Jacobi-Belman equation. This article formulates an neural optimal adaptive backstepping control programme for FO chaotic circuit systems with state constraints. To avoid states exceeding constraints during optimal control, a scheme combining a transformation formula with a nonlinear state dependent function is first developed, and then the original system is transformed into an integer-order unconstrained one. To achieve optimal control, a reinforcement learning adaptive backstepping control based on the transformation scheme is introduced, where weight update laws of the reinforcement learning are constructed based on the negative gradient of a positive function rather than the square of Bellman residual, which effectively simplifies the form and design process of the update laws. According to the stability analysis, the formulated programme assures that all signals are bounded and states remain within the specified constraint space. Eventually, a simulation case is displayed to demonstrate the validity of the developed approach.

In this paper, we address a novel and comprehensive social welfare maximization (SWM) problem for the optimal energy management in a smart grid. The objective is to maximize the total social welfare of dispatchable devices in the smart grid while satisfying certain constraints. Each device in the smart grid is required to meet its local power constraints, and the system as a whole maintains supply-demand balance, taking into account transmission losses and stochastic output power. To facilitate distributed algorithm design, we initially transform the SWM problem into an equivalent dual problem, which is a distributed composite optimization problem. Subsequently, a novel fully distributed proximal alternating direction method of multipliers (PADMM) is proposed, where each agent can autonomously select non-coordinated step size parameters based solely on local information, independent of other agents and the network structure. Detailed convergence analysis is provided, and a worst-case$\mathcal{O}(1/k)$convergence rate is established in the non-ergodic sense. Finally, several numerical experiments are conducted to confirm the effectiveness of the proposed algorithm.

EinStein würfelt nicht! is a perfect information stochastic game in which randomness influences the game process enormously. In this paper, we propose an optimized algorithm named Quick Neural Network Tree Search (QNNTS) based on deep reinforcement learning and Monte Carlo tree search (MCTS) to construct the artificial intelligence agent of EWN. Meanwhile the lightness of the model makes it possible to train with much less computing resources. The optimization structure of the algorithm based on MCTS is named Optimized UCT with Heuristic Search (UCTH), which introduces the expectation valuation strategy into the Monte Carlo tree search. As the prerequisite product of QNNTS, it performs with an improvement of the winning rate. Ultimately, the Attention-ResNet structure combined with domain knowledge is used to obtain the proposed algorithm. Compared with several conventional algorithms, it gains high winning rates of at least 68%.

The emergence of a news recommendation system can effectively improve the news reading experience of users. The most important task of the system is how to learn news and user representations accurately. In the process of learning news representations, most of the current research works do not fully utilize the news features, which makes it difficult to learn more comprehensive news representations. Most research work only learns user representations from a single perspective, which may not be sufficient to learn diverse and dynamic user representations. Therefore, we propose a news recommendation system with a multiview graph convolutional network (NRMG). It contains two parts: news representation and user representation. The knowledge–content collaboration network is adapted to learn news representations from news content and entities, while the multiview graph convolutional network (GCN) is utilized to learn user representations from the user’s click history. The advantage of the NRMG system is that we not only expand the available features by constructing a subclass knowledge graph (KG), but also effectively improve the ability of the news recommendation system to accurately learn news and user representations. Experimental results on the real dataset MIcrosoft News Dataset (MIND) show that the NRMG achieves a 2.25% improvement in area under the receiver operating characteristic (ROC) curve (AUC) value compared with state-of-the-art methods.

Dimensional sentiment analysis (DSA) aims to recognize continuous real-valued annotations in multidimensional spaces such as valence-arousal space. It can serve as a sentiment analysis that is more fine-grained than traditional polarity classification. Existing methods primarily focused on supervised learning, which requires a large number of training samples. To address this issue, recent studies have suggested the use of a generative model for semi-supervised DSA. However, these methods lack the exploration of relationships between samples, which may limit their effectiveness. In this study, we proposed a sample relationships (SRs) model for semi-supervised DSA. Compared with the conventional methods, the SR module can integrate the interactive information from different samples, effectively capturing their interdependencies. The results of experiments conducted on three datasets indicated that the performance of the proposed method was better than those of the existing models and that the performance was effective and competitive even with insufficient data.

This article investigates the event-triggered impulsive control (ETIC) problem for a class of nonlinear time-delay systems subject to exogenous disturbances. An original event-triggered mechanism (ETM) which utilizes the information of system state and external input is constructed based on Lyapunov function approach. To achieve the input-to-state stability (ISS) of the considered system, some sufficient conditions are presented, in which the underlying relationship among ETM, exogenous input, and impulse action is established. Furthermore, the possible Zeno behavior induced by the proposed ETM is excluded simultaneously. As an application, the design criterion of ETM and impulse gain is put forward for a class of impulsive control systems with delay according to the feasibility of some linear matrix inequalities (LMIs). Finally, two examples with numerical simulations are provided to confirm the effectiveness of the developed theoretical results, where the synchronization issue of delayed Chua's circuit is considered.

This technical paper investigates the cluster synchronization of finite-field networks (FFNs) based on the algebraic state space representation. By resorting to the semi-tensor product of matrices, the cluster synchronization of an FFN can be completely converted into the set stability of state trajectory. Then, a necessary and sufficient condition for the cluster synchronization of FFNs is obtained based on the invariant subset and one-step transition matrix. In particular, the obtained results are applicable to check the leader-follower synchronization and group consensus of FFNs. Finally, a numerical example is given to illustrate the feasibility of the obtained results.

This technical paper utilizes the Lyapunov theory to characterize the event-triggered set stabilizability of Markovian jump logical control networks (MJLCNs). Whereas the existing result for checking the set stabilizability of MJLCNs is only sufficient, this technical paper further establishes its necessary and sufficient condition. First, the Lyapunov function is established to describe the set stabilizability of MJLCNs necessarily and sufficiently by combining recurrent switching modes and desired state set. Then, the triggering condition and the input updating mechanism are designed regarding the value change of the Lyapunov function. Finally, the effectiveness of theoretical results is demonstrated by a biological example concerning the lac operon in Escherichia coli.

Presents corrections to the paper, (Correction for "Consensus in High-Power Multiagent Systems With Mixed Unknown Control Directions via Hybrid Nussbaum-Based Control").

This article proposes a predefined-time leader-following consensus control scheme for a second-order multiagent system (MAS) whose communication network is subject to channel fading. New distributed observers are designed to achieve prescribed-time leader's states estimation under undirected graph and digraph over faded communication channel. Then, a new adaptive dynamic surface predefined-time control is developed for the elimination of the mismatched disturbances stemmed from estimation error and achieving practical predefined-time leader-following consensus. It is proved that the developed control approach achieves predefined-time consensus tracking. This article's contribution is to propose novel distributed observers to remove the influence of channel fading and estimate leader's states under undirected graph and digraph within prescribed time and develop a novel predefined-time control to achieve predefined-time consensus tracking over fading channel. A simulation example verifies that the designed control scheme is effective.

In this study, asynchronous sliding-mode control (SMC) for discrete-time networked hidden stochastic jump systems subjected to the semi-Markov kernel (SMK) and cyber attacks is investigated. Considering the statistical characteristic of the SMK, which is challenging to acquire in engineering, this study recognizes the SMK to be incomplete. Due to the mode mismatch between the original system and the control law in the operating process, a hidden semi-Markov model is proposed to describe the considered asynchronous situation. The main aim of this study is to construct an asynchronous SMC mechanism based on an incomplete SMK framework under the condition of random denial-of-service attacks so that the resulting closed-loop system can realize the mean-square stability. By virtue of the upper bound of the sojourn time in each mode, innovative techniques are developed for mean-square stability analysis under an incomplete SMK. Furthermore, an asynchronous SMC scheme is designed to achieve the reachability of the quasi-sliding mode. Finally, the effectiveness is verified using an electronic throttle model.

This article investigates a novel neuro-adaptive barrier Lyapunov function (BLF)-based event-triggered preassigned finite-time consensus control with asymptotic tracking for the nonlinear multiagent systems. The proposed approach is designed to broaden the scope of application by considering the high-order nonstrict-feedback dynamics of each agent with dynamic uncertainties subject to external disturbances and nonaffine nonlinear faults. A neural network (NN) is employed to approximate the unknown nonlinear terms. By fusing the NNs and Butterworth low-pass filter technique, the issues arising from the nonaffine nonlinear fault are addressed. To save the communication resources, a novel dynamic event-triggered mechanism based on an enhanced switching threshold is suggested. Additionally, a novel concept called the preassigned finite-time performance function (PFTPF) is defined to improve the transient and steady-state performances as well as providing faster response. The key feature of the proposed adaptive BLF-based control based on the bound estimation method is the introduction of a smooth function with decreasing variable which not only ensures that all the signals remain bounded and the synchronization errors are restricted within the PFTPF but also guarantees that the tracking errors asymptotically converge to zero. Finally, an illustrative example is provided to verify the feasibility of the proposed control approach.

This work involves the sliding mode control (SMC) issue for a class of Markov jump singularly perturbed systems (MJSPSs) under consideration of unmatched external disturbances and communication constraints. For the first time, the piecewise homogeneous Markov chain (MC) which depends on the system mode and the controller mode is applied to control the scheduling of stochastic communication protocol (SCP), so that the MCs in the system models, the controller and the SCP constitute a three-layer nonstationary Markov model (NMM). This model perfectly describes the different objects of the three MCs and reflects the mutual regulation among them. The critical issue is to devise an adaptive controller and a sliding surface (SS) simultaneously under SCP scheduling. By applying a standard singular sliding mode surface, an appropriate nonstationary SMC law is established to promise the accessibility of the SS and the stability of the closed-loop system (CLS), and meet the expected performance indicator. Further, using the mode-dependent Lyapunov function and piecewise homogeneous Markov model method, sufficient criteria are obtained. The specific expression of the control gain is obtained by matrix decoupling technology. Finally, a numerical simulation is furnished to testify the correctness of the conclusion.

A novel reinforcement learning-based predefined-time tracking control scheme with prescribed performance is presented in this article for nonlinear systems in the presence of external disturbances. First, by employing the backstepping strategy, an adaptive optimized controller is developed under the identifier-critic-actor framework. Therein, the unknown nonlinear dynamics and the system control behavior can be learned effectively through neural networks. Moreover, aiming at obtaining the preset tracking performance, the prescribed performance control is integrated with the predefined-time control. In contrast to previous studies, the proposed scheme can not only constrain the tracking error rapidly to a prearranged vicinity of origin, but also ensure that the upper bound of convergence time can be adjusted in advance via a separate control parameter. In terms of the predefined-time stability theory, the boundedness of all system states can be proven within a predefined time. Finally, the availability and improved performances of the proposed control scheme are demonstrated by a numerical example and a single-link manipulator example.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem, which introduces colors to distinguish the accessibility of its cities to salesmen. This work proposes a city/customer-centric model called cumulative capacitated CTSP (C2-CTSP) to tackle some practical problems with fast response requirements. Its hypergraph and mathematical programming formulations are developed for the first time. A general variable neighborhood search (GVNS) metaheuristic is designed to solve it. Specifically, greedy backtracking is proposed to initialize a solution taking into account the cumulative cost and two constraints including colors and capacities. Next, 2-swap, reinsertion, and double-bridge operations are randomly selected and carried out to execute the perturbation. Moreover, neighborhood-list-2-opt, relocation move, and generalized partition crossover are organized as variable neighborhood descent to constitute the local search for better solutions. Extensive experiments are conducted to compare the proposed GVNS with four genetic algorithms, two hybrid ant colony systems, two variable neighborhood search methods, and a perturb-based local search in 20 regular and random cases. The statistical results demonstrate that GVNS is superior to all competitors tuned by irace package in terms of both search ability and convergence rate. In addition, the study of six GVNS variants lacking different operators validates the significant role of each corresponding operator in GVNS's outstanding performance.

This article presents a comprehensive solution to mitigate network congestion in T-S fuzzy networked control systems caused by denial-of-service (DoS) attacks and quality-of-service (QoS) queuing mechanisms. We develop a novel data compression mechanism to alleviate network congestion and use a mini-batch descent gradient algorithm to optimize trigger thresholds, thereby reducing bandwidth usage. In addition, we introduce asymmetric Lyapunov–Krasovskii functions to decrease the number of decision variables, which improves the reliability and robustness of the control algorithm. Finally, we propose an intelligent event-triggered controller supervised by mini-batch machine learning and validate it on the joint CarSim–Simulink platform. Experimental results demonstrate that our approach reduces the sensitivity of autonomous vehicle systems to network fluctuations while ensuring system stability under network congestion caused by DoS attacks.

This article mainly investigates the consensus control of fractional-order multi-agent systems (FOMASs) with partially unknown control directions and saturated inputs, where error dynamics between the leader and all followers are characterized via a fractional-order Takagi–Sugeno fuzzy control strategy incorporating “IF–THEN” rules. A novel Nussbaum function is employed to handle with partially unknown control directions. Consensus performance is derived based on fractional-order Lyapunov stability criteria. The proposed control strategy not only ensures asymptotic consensus of FOMASs, but also makes all closed-loop signals uniformly bounded. A chief merit is avoiding solving cumbersome linear programming hassles, which lightens the computational burden. Besides, the raised scheme is generalized to handle with the formation control of FOMASs. Finally, the authenticity of the theory is confirmed through numerical simulations.

This paper aims to consider the finite-time synchronization of a class of discontinuous fuzzy competitive networks with non-differential leakage delays. Different from the previous competitive networks and leakage-delayed networks, the activation functions are discontinuous and the leakage delays are non-differential. Without using the Lyapunov function methods, the synchronization in a finite-time is investigated based on the comparison principle and inequality techniques, which are used for the first time in the study of the competitive networks and leakage-delayed networks. Moreover, the feedback controller and bounded feedback controller are designed to help achieve the finite-time synchronization. The bounded feedback controller is formulated by the functionarctanand a new inequality lemma is given, which plays a vital role in achieving the synchronization via bounded feedback controller. Although both controllers can help achieve the finite-time synchronization, it can be found that there is a big difference that the value of feedback controller is greatly influenced by system's state variables, but bounded controller is not. Such fact leads to different settling-times. The conclusion is that bounded controller has more advantages in shortening convergence time through numerical simulations.

In this paper, a novel reinforcement learning-based predefined-time control method for nonlinear servo systems with prescribed performance is proposed under an event-triggered strategy. Firstly, the nonlinear dynamics and control behaviors of the systems can be trained effectively through fuzzy logic systems under the identifier-critic-actor framework. Moreover, by employing the prescribed performance control and a switching event-triggered rule, system tracking performance can be ensured while decreasing the data transmission frequency. With the assistance of the predefined-time stability criteria, the boundedness of system variables and the convergence of tracking errors within a predetermined time can be guaranteed. Comparisons with some existing control schemes are addressed regarding tracking performance and action costs. The availability and superiority of the suggested scheme are verified in the simulations.

In the traditional constrained control of nonlinear systems, the controller design usually requires that the initial value of the system meets certain strict conditions, and generally only considers static constraints. This article concentrates on the issue of adaptive fuzzy echo state network decentralized control for fractional-order (FO) large-scale nonlinear systems with strong interconnections and time-varying deferred constraints. With the backstepping technique, an FO fuzzy echo state network is constructed to approximate unknown nonlinear functions and interconnected terms in each step, which greatly removes some additional assumptions on unknown functions and provides a higher degree of design freedom and stronger robustness. A shifting function and an error transformation scheme are introduced to handle constraints against the unknown initial tracking condition. Moreover, the constraint conditions are satisfied within a specified time even if they are violated initially by using a time-varying barrier Lyapunov function. Especially, an equivalent definition of the bivariate convex function is given, and an inequality is constructed, which can be used to analyze the stability of FO systems by constructing a bivariate Lyapunov function. According to the FO Lyapunov stability theorem, the proposed adaptive controller can ensure that all the signals involved remain bounded and the tracking error possesses a fast convergence. Finally, an example of the FO single-machine-infinite bus power system illustrates the effectiveness of the proposed control strategy.

This article investigates the synchronization control problem of fuzzy complex dynamic networks (FCDNs) with diversified network attacks. By making full use of the weighted historically released packets, a novel anti-attack memory-based adaptive event-triggered protocol (AETP) and the corresponding anti-attack fuzzy memory-based controller are introduced by taking the sporadic denial-of-service (DoS) and deceptive attacks into consideration concurrently. Compared with the conventional event-triggered protocols and AETPs, the developed anti-attack memory-based AETP can not only effectually reduce the frequency of event triggers, but also enhance the transient performance of FCDNs. In addition, in view of a new sampling-instant-dependent piecewise Lyapunov functional, less conservative criteria are induced to guarantee that the stochastically exponential synchronization of FCDNs is accomplished in the case where the deceptive and sporadic DoS attacks coexist. Finally, the merits and validity of the theoretical results are illustrated through numerical examples.

This article is centered on the synchronization issue of fuzzy reaction–diffusion dynamic networks (RDDNs) with multiple cyberattacks. A novel attack-resilient dynamic event-triggered policy and the relevant fuzzy attack-resilient controller are designed via considering the deception and irregular (aperiodic) denial-of-service (DoS) attacks simultaneously. Based on a novel piecewise Lyapunov–Krasovskii functional with the realistic sampling information and some inequality techniques, some criteria are derived to ensure that the synchronization of fuzzy RDDNs can be realized in the presence of the deception and irregular DoS attacks. Lastly, simulation examples containing the colour image encryption/decryption are supplied to exhibit the advantages and practicality of the developmental theories.

This article focuses on fuzzy structural adaptive optimal control issue of discrete-time nonlinear complex networks (CNs) via adopting the reinforcement learning (RL) and Takagi–Sugeno fuzzy modeling approaches, where the control gains are subjected to structured constraints. In accordance with the Bellman optimality theory, the modified fuzzy coupled algebraic Riccati equations (CAREs) are constructed for discrete-time fuzzy CNs, while the modified fuzzy CAREs are difficult to solve directly through mathematical approaches. Then, a model-based offline learning iteration algorithm is developed to solve the modified fuzzy CAREs, where the network dynamics information is needed. Moreover, a novel data-driven off-policy RL algorithm is given to compute the modified fuzzy CAREs, and the structural optimal solutions can be obtained directly by using the collected state and input data in the absence of the network dynamics information. Furthermore, the convergence proofs of the presented learning algorithms are provided. In the end, the validity and practicability of the theoretical results are explicated via two numerical simulations.

This study examines observer-based sliding mode control for stochastic sampling Takagi–Sugeno fuzzy systems under a stochastic communication protocol (SCP). Aimed at reducing network bandwidth use and lessening the negative effects of signal transmission disruptions, the sampling signal sequence exhibits dual stochastic characteristics, influenced by both stochastic sampling and network attacks. The stochastic intervals between samples are governed by a Markov chain, and the samples are vulnerable to denial-of-service attacks during network transmission. Moreover, a novel SCP is developed utilizing sojourn probability and residence time information, enhancing communication efficiency. Subsequently, a suitable sliding mode control law is crafted based on the observed data, providing a sufficient condition for the mean square exponentially ultimate boundedness of the closed-loop system. The efficacy of the constructed methodology is demonstrated using a tunnel diode circuit model.

Unlike conventional integer-order backstepping technique, this paper provides an observer-based event-triggered adaptive fuzzy fractional backstepping control method for uncertain integer-order multiagent systems with the target of reaching a leader-follower consensus protocol. Through employing the fuzzy logic system with arbitrary approximation accuracy to unknown nonlinear functions, a fuzzy state observer is devised to estimate unmeasurable states that are not completely available. Fractional calculus with arbitrary non integer orders operator is introduced into the design of control laws to further improve system performance. An event-triggered mechanism with freely adjustable threshold variable is established to debase energy wastage and computation cost. Theoretical analysis verifies that the proposed method can guarantee that the internal signals of the entire closed-loop system are ultimately uniformly bounded and the synchronization errors gradually converge to a small neighboring area of the origin. Besides, Zeno's behavior is excluded. Finally, the authenticity and reliability of the theory are demonstrated by an actual inverted pendulum model.

Pansharpening aims at merging the spectral information from a low-resolution multispectral (LRMS) image with the spatial details from a panchromatic (PAN) image to produce a high-resolution multispectral (HRMS) image. Regrettably, existing techniques tend to concentrate on utilizing spectral and spatial information at a single resolution to reconstruct HRMS images, which leads to a deficiency in fully exploiting the semantic information from different resolution levels. In consideration of the aforementioned issues, we proposed a deep multiresolution representation framework for pansharpening, termed DMR-Pan. With the idea of maintaining high-resolution (HR) and low-resolution (LR) representations, we proposed an effective strategy for the extraction of multiresolution semantics, where a PAN branch and an LRMS branch operate in parallel to not only retain HR spatial details and spectral information but also extract multilevel semantics from different resolutions. Through cross-modality and cross-resolution guidance mechanisms, the extracted multiresolution semantics are aggregated with minimal information loss. Finally, a novel query fusion mechanism is introduced to capture the latent interdependency between dual modalities with cross-modality channel-group attention (CCGA), thereby maximizing complementary semantics and significantly improving the fusion ability of the framework. Rigorous experimentation conducted on multiple datasets illustrates that our DMR-Pan surpasses comparable techniques both in qualitative and quantitative assessments.

This study investigates the stability problem in nonlinear networked control systems (NCSs). First, innovative compression rules are introduced to mitigate network congestion and bandwidth utilization issues stemming from quality of service (QoS) queuing mechanisms and denial of service (DoS) attacks. We develop an intelligent trigger controller supervised by a mini-batch machine learning (MBML) algorithm to optimize network bandwidth utilization. Furthermore, we formulate more generalized Lyapunov-Krasovskii functions (LKFs) to simplify mathematical derivations, and we employ appropriate integral inequalities to minimize constraints. Finally, experimental evaluations are conducted on an autonomous vehicle (AV) using the joint CarSim-Simulink platform to verify the effectiveness of the proposed intelligent trigger controller.

This article considers the rapid vibration reduction problem of macro–micro composite positioning stage (MMCPS) using an adaptive neural preassigned-time control strategy. Based on Newton's second law, the MMCPS is modeled as an interconnected system with unknown perturbations, and for the first time, the vibration reduction problem of MMCPS is transformed into a displacement constraint problem. Through adaptive neural network approximation and backstepping control, a preassigned-time controller with a novel performance function-related term is developed, which not only significantly improves the positioning accuracy and reduces the vibration amplitude but also ensures that the displacements of the voice coil motor axis and the stage are constrained to a predefined region in a finite time. Another distinguished feature of the proposed controller lies in the fact that the settling time of the displacement signals can be set as an arbitrary positive value. Moreover, all signals of the closed-loop system are proved to be semiglobally uniformly ultimately bounded. Finally, the feasibility of the designed control strategy is demonstrated via a simulation experiment.

This short paper addresses the challenge of network congestion in T-S fuzzy networked control systems (NCSs) caused by denial of service (DoS) attacks and quality of service (QoS) queuing mechanisms. Firstly, a novel signal compression mechanism is introduced to mitigate network congestion. The trigger threshold is optimized using a mini-batch descent gradient algorithm, effectively reducing bandwidth utilization. Furthermore, tailored Lyapunov-Krasovskii functions (LKFs) are established for the system, and we propose an intelligent event-triggered controller (IETC) under machine learning supervision. Finally, the effectiveness of the proposed approach is demonstrated through rigorous verification on the joint CarSim-Simulink platform.

Emerging technologies of connected and automated vehicles (CAVs) applied at intersections have great potential to improve traffic efficiency, driving safety, and fuel economy. This paper proposes a virtual spring strategy for coordinating CAVs to pass through signal-free intersections. A virtual spring coordination system (VSCS) is established to force the movements of conflicting CAVs in terms of spring characteristics. Inspired by the properties of springs with dampers, a distributed control protocol is designed to regulate CAVs to reach a desired state of motion when crossing intersections. For ensuring the convergence of the VSCS, i.e., the total elastic potential energy approaches to zero, sufficient conditions for the internal stability are derived subject to the input saturation. To further improve the anti-disturbance capability of the VSCS, we ensure the upper bound of the disturbance propagation, which is characterized by an$H_{\infty }$performance index. The proposed strategy is developed in a receding horizon framework and tested under the two scenarios in simulations. The simulation results show the effectiveness and superiority of the proposed strategy.

Void underneath a slab is a common damage type in airport cement concrete pavement. Existing methods mainly use ground penetrating radar (GPR) images and deep learning to detect the voids but face two problems: inherent limitation of image-wise deep learning and imperfect GPR datasets. This study has proposed a signal-wise cascade deep network (SWC-net) to detect voids underneath slabs, which was trained by a dataset with real-world and simulation GPR signals. In the study, a multi-phase heterogeneous pavement model was first built, in which each pavement layer was reconstructed based on the real-world morphologies and distributions of aggregates and other materials. The model was then used for the finite-difference time-domain (FDTD) simulation of GPR signal propagation. The simulation signals were merged with real-world GPR signals to generate a void signal dataset covering comprehensive pavement conditions. Finally, the proposed SWC-net was trained by the merged dataset to detect voids. The inputs of the network were the GPR signals, while the outputs were the signal classes and abnormal intervals of voids to perform signal-wise void detection. The experiments on Nanjing Dajiaochang and Xuzhou Guanyin airports showed that the FDTD results generalized the signal dataset by simulating different real-world conditions, while the proposed network outperformed the image-wise deep networks on void detection.

This article investigates the resilient proportional-integral observer (PIO) problem for Markov switching memristive neural networks (MSMNNs) with randomly occurring sensor saturation within a finite-time interval. The Markov switching of memristive neural networks is regulated by a higher level deterministic switching signal, whose transition probabilities are piecewise time-varying and can be depicted by the average dwell-time strategy. Meanwhile, a Bernoulli stochastic process associated with an uncertain packet arriving rate is adopted to describe the randomly occurring sensor saturation. The aim is to design a resilient PIO such that the augmented dynamic has the property of stochastic finite-time boundedness while meeting the desired performance index. By applying the Lyapunov method and the average dwell-time scheme, sufficient criteria are established for MSMNNs, and a unified design method is presented for the existence of the PIO. Lastly, the attained theoretical results are validated via a numerical simulation.

In this article, we establish the local and global exponential convergence of a primal-dual dynamics (PDD) for solving equality-constrained optimization problems without strong convexity and full row rank assumption on the equality constraint matrix. Under the metric subregularity of Karush-Kuhn-Tucker (KKT) mapping, we prove the local exponential convergence of the dynamics. Moreover, we establish the global exponential convergence of the dynamics in an invariant subspace under a technically designed condition which is weaker than strong convexity. As an application, the obtained theoretical results are used to show the exponential convergence of several existing state-of-the-art primal-dual algorithms for solving distributed optimization without strong convexity. Finally, we provide some experiments to demonstrate the effectiveness of our results.

Polyadenylation [Poly(A)] is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial-temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.

The synchronization problem of the coupled delayed inertial neural networks (DINNs) with stochastic delayed impulses is studied. Based on the properties of stochastic impulses and the definition of average impulsive interval (AII), some synchronization criteria of the considered DINNs are obtained in this article. In addition, compared with previous related works, the requirement on the relationship among the impulsive time intervals, system delays, and impulsive delays is removed. Furthermore, the potential effect of impulsive delay is studied by rigorous mathematical proof. It is shown that within a certain range, the larger the impulsive delay, the faster the system converges. Numerical examples are provided to show the correctness of the theoretical results.

This article studies synchronization issues for a class of discrete-time fractional-order quaternion-valued uncertain neural networks (DFQUNNs) using nonseparation method. First, based on the theory of discrete-time fractional calculus and quaternion properties, two equalities on the nabla Laplace transform and nabla sum are strictly proved, whereafter three Caputo difference inequalities are rigorously demonstrated. Next, based on our established inequalities and equalities, some simple and verifiable quasi-synchronization criteria are derived under the quaternion-valued nonlinear controller, and complete synchronization is achieved using quaternion-valued adaptive controller. Finally, numerical simulations are presented to substantiate the validity of derived results.

This article is concerned with average consensus of multi-agent systems via intermittent event-triggered strategy. First, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality for the condition is established. Using the established inequality, several criteria on average consensus are obtained. Second, the optimality has been investigated based on average consensus. The optimal intermittent event-triggered strategy in the sense of Nash equilibrium and corresponding local Hamilton-Jacobi-Bellman equation are derived. Third, the adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are also given. Finally, two numerical examples are presented to show the feasibility and effectiveness of our strategies.

This article addresses the stabilization and synchronization problems of coupled neural networks (NNs) via an impulsive adaptive control (IAC) strategy. Unlike the traditional fixed-gain-based impulsive methods, a novel discrete-time-based adaptive updating law for the impulsive gain is designed to maintain the stabilization and synchronization performance of the coupled NNs, where the adaptive generator only intermittently updates its data at the impulsive instants. Several stabilization and synchronization criteria for the coupled NNs are established based on the impulsive adaptive feedback protocols. Additionally, the corresponding convergence analysis are also provided. Finally, the effectiveness of the obtained theoretical results is illustrated using two comparison simulation examples.

In this brief, we consider the stability of inertial memristor-based neural networks with time-varying delays. First, delayed inertial memristor-based neural networks are modeled as continuous systems in the flux-current-voltage-time domain via the mathematical model of Hewlett-Packard (HP) memristor. Then, they are reduced to delayed inertial neural networks with interval parameters uncertainties. Quasi-equilibrium points and quasi-stability are proposed. Quasi-stability criteria of delayed inertial memristor-based neural networks are obtained by matrix measure method, the Halanay inequality, and uncertainty technologies. In the end, a numerical example is provided to show the validity of our results.

Although the immune effector CD8 T cells play a crucial role in clearance of viruses, the mechanisms underlying the dynamics of how CD8 T cells respond to viral infection remain largely unexplored. Here, we develop a delayed model that incorporates CD8 T cells and infected cells to investigate the functional role of CD8 T cells in persistent virus infection. Bifurcation analysis reveals that the model has four steady states that can finely divide the progressions of viral infection into four states, and endows the model with bistability that has ability to achieve the switch from one state to another. Furthermore, analytical and numerical methods find that the time delay resulting from incubation period of virus can induce a stable low-infection steady state to be oscillatory, coexisting with a stable high-infection steady state in phase space. In particular, a novel mechanism to achieve the switch between two stable steady states, time-delay-based switch, is proposed, where the initial conditions and other parameters of the model remain unchanged. Moreover, our model predicts that, for a certain range of initial antigen load: 1) under a longer incubation period, the lower the initial antigen load, the easier the virus infection will evolve into severe state; while the higher the initial antigen load, the easier it is for the virus infection to be effectively controlled and 2) only when the incubation period is small, the lower the initial antigen load, the easier it is to effectively control the infection progression. Our results are consistent with multiple experimental observations, which may facilitate the understanding of the dynamical and physiological mechanisms of CD8 T cells in response to viral infections.

Distributed aggregative optimization (DAO) is a special class of optimization problems of networking agents where the local objective function of each agent relies on the aggregation of other agents’ decisions as well as its own. It is widely known that convergence rate is one of the most important evaluation indexes for practical applications of DAO algorithms. However, it is challenging to achieve fast convergence rate for DAO algorithms over a directed graph, owing to the fact that the underlying interaction graph may be unbalanced, and the local objective functions of individual agent depend upon the decisions of other ones. To efficiently solve the DAO problem over directed graphs, a kind of accelerated distributed optimization algorithm with Nesterov momentum and constant step-size is designed and analyzed. To handle the effect of unbalanced property of the directed networks on solving the DAO problem, a consensus iteration law is embedded into the optimization algorithm to estimate the left Perron eigenvector of the weight matrix. Furthermore, a kind of distributed aggregative gradient tracking technology associated with the Nesterov momentum coefficient is developed and employed to construct the accelerated distributed aggregative algorithm. It is theoretically shown that the proposed optimization algorithm could yield a favorable linear convergence rate after limited iterative steps when the global objective function is$\mu$-strongly convex and$L_{1}$-smooth. At last, numerical experiments are provided to confirm the findings.

In this article, we consider exponential estimation and passivity of memristive neural networks with quaternion parameters. A Takagi–Sugeno type rule is introduced into the quaternion memristive neural networks, which makes the system much easier. To achieve the exponential stability of the estimation error system, a proper controller is designed, which derived in the two norm form. Further, the quasi-state estimation condition is also considered. Along with Lyapunov theory, some criteria are obtained to achieve the exponential passivity of the discussed system.

In this study, the partial-node-based state estimator design problem is addressed for complex networks subject to sojourn probabilities and a memory-based dynamic event-triggered protocol (DETP). Based on nonhomogeneous sojourn probabilities, a new switching law is presented to describe the dynamic behavior of the network topology, where the deterministic switching signal regulates the proper feedback sojourn probability information. Using historically transmitted packets, a novel memory-based DETP in the discrete-time domain was built simultaneously to alleviate energy consumption and improve system performance. On the basis of Lyapunov theory, the augmented dynamics are stochastically stable in the presence of a preset performance level. To this end, two simulation examples were presented to verify the validity and applicability of the developed methodology.

This article considers some further results on fixed-time leader-following consensus of disturbed heterogeneous multiagent systems (DHMASs). Both the first-order (FO) nonlinear nodes and second-order (SO) ones are contained in the considered system model. First, a distributed observer is developed to ensure the estimated states converge to the leader’s real states under a directed graph. Second, an integral sliding mode controller (SMC) is proposed to guarantee the predefined-time consensus (PTC) of FO agents based on the Gudermannian functions. Third, a nonsingular terminal SMC is given to realize consensus of SO agents in a predefined time. It is pointed out that, both the reaching and sliding time can be preset as to the task requirements. Finally, the effectiveness of new designs are illustrated by two numerical examples.

In this article, the heterogeneity of the electromagnetic field is taken into account and thus the diffusion effect is introduced into the artificial neural network modeling. The first attempt of a class of large-scale bidirectional associative memory neural networks is provided, incorporating diffusion and delays. Using Coates’ flow diagram is able to efficiently and accurately capture the characteristic equations of large-scale reaction-diffusion neural networks. Furthermore, by tracing the distribution of characteristic roots driven by the time delay, a criterion on the local stability is determined and the critical tipping point caused by Hopf bifurcation is also predicted, respectively. Numerical simulations are eventually conducted to demonstrate the practical implications of the theory. It is shown that spatiotemporal dynamic behaviors of neural networks suggested are significantly affected by the transmission delay, the system scale, the self-feedback coefficient and the diffusivity.

This article investigates the passivity-based fuzzy control problem for discrete-time nonlinear semi-Markov jump singularly perturbed systems (SPSs) by the semi-Markov kernel (SMK) approach. Due to the fact that the statistical information of SMK is difficult to acquire completely, the generally partially available case, covering the completely available and completely unavailable cases, is considered to remove the ideal assumption that the system information is invariably available to controllers. Additionally, the mode-dependent fuzzy rules describing nonlinear behavior are constructed, that is, the membership functions are dependent on the system modes, thus promoting the previous results efficacious in a more accurate form. Subsequently, by fully utilizing available SMK information, some sufficient conditions based on the slow state feedback technique are derived to ensure that the semi-Markov jump fuzzy SPSs are stabilized and satisfy the given passive performance. Finally, the superiority and practicability of the presented theoretical results are explained by a numerical example and an inverted pendulum model.

Finite/fixed-time controls for multiagent systems (MASs) in cooperative and competitive networks have been discussed intensively, and the structural balance and gauge transformation play a crucial role in analyzing the finite/fixed-time bipartite cooperative behavior of MASs. However, the structural balance for a large-sized network is not easy to verify, and it is uncertain whether the gauge transformation is feasible for nonlinear networks. Without adopting the gauge transformation, this article considers finite/fixed-time bipartite controls of nonlinear neural networks (NNs) with cooperative and competitive interactions. A matrix$M$in cooperative networks is extended to a general one in cooperative and competitive networks to design quadratic Lyapunov functions. On this basis, the finite/fixed-time bipartite synchronization criteria and adjustment times are obtained, which avoids adopting the gauge transformation in a nonlinear system. The negative cycle is found to have a very important effect on the dynamic behavior of NNs. Numerical examples validate the proposed bipartite controls of NNs in a signed graph.

This article investigates the retarded differential equation on time scales and presents several criteria on uniform (asymptotic) stability by the relaxed Lyapunov–Razumikhin method. Allowing the Lyapunov–Razumikhin function (LRF) to possess an indefinite or positive definite$\Delta $-derivative is the main novelty of this article. Furthermore, the obtained uniform (asymptotic) stability criteria are used to solve the stabilization problem of single-species system with time delay. As an application of the established stability criteria, we present one numerical example of a uniformly stable single-species system.

This article investigates the global exponential stability of the nonlinear delayed systems with logically selected impulses. First, the mathematical model for a new kind of hybrid systems is proposed. Based on the semi-tensor product of matrices, several sufficient conditions for globally exponential stability are derived. The impulsive switching signals are generated by two types of logical dynamical systems of addressed systems. To be specific, one signal is given in advance, and the other one is determined by some impulsive cycle sequences. An algorithm is developed to design logical dynamical systems for determining impulsive signals. The effectiveness of the main results is verified by two examples.

This article focuses on prescribed-time control for nonlinear switched systems using event-triggered impulsive control. Taking into account different characteristic among each mode, two types of mode-dependent event-triggered mechanisms are presented with mode-dependent Lyapunov functions and tunable exponential parameters, which substantially reduce the update times of controller. It shows that, the interevent times, under the designed mode-dependent event-triggered mechanism, have a uniform lower bound and then do not exhibit Zeno behavior. Further, using Lyapunov approach, some sufficient conditions for prescribed-time stability are obtained, where a relationship among triggering parameter, impulse strength, and prescribed time is established explicitly. As an application, the proposed methods are proved to be applicable to switched control systems, where a class of LMI-based hybrid control approaches, including switching control and event-triggered impulsive control, is developed. The effectiveness of our presented event-triggered impulsive control is illustrated through two examples.

This article analyzes the frequency regulation problems of load frequency control (LFC) system under some unforseen network environments, such as unexpected load, mixed attacks, and time delay in cyber–physical system (CPS). A dual-link strategy using alternating current and direct current tie-line transmission technologies is employed to improve the efficiency of power exchange and retain the power system frequency stability. Then, an improved integral-based adaptive event-triggered control (IA-ETC) with flexible threshold parameters is designed using the historical average system state to tackle the sudden fluctuations with improved communication efficiency. Furthermore, the Lyapunov–Krasovskii functional (LKF) with delay terms is proposed to investigate the exponential stability with$H_{\infty}$performance on the dual-link LFC system. Finally, simulation results show that this development presents an improved frequency regulation performance.

This paper proposes two novel distributed continuous-time algorithms inspired by PID control to solve distributed optimization problems. The algorithms are referred to as first-order and second-order, respectively, depend on the intrinsic dynamics of the agents in the network. Sufficient conditions are derived so that both algorithms converge exponentially over undirected connected graphs. Finally, numerical simulations illustrate the effectiveness and efficiency of the proposed algorithms.

Abstract In the non‐homogeneous texture synthesis task, the overall visual characteristics should be consistent when extending the local patterns of the exemplar. The existing methods mainly focus on the local visual features of patterns but ignore the relative position features that are important for non‐homogeneous texture synthesis. Although these methods have achieved success on homogeneous textures, they cannot perform well on non‐homogeneous textures. Thus, it is desirable to model the dependence between pixels to improve the synthesis performance. To ensure synthesis results from both the local detail structure and the overall structure, this paper proposes a non‐homogeneous texture extended synthesis model (GAGCN) combining the generate adversarial network (GAN) and the graph convolutional network (GCN). The GAN learns the internal distribution of image patches, which makes the synthetic image have rich local details. The GCN learns the latent dependence between pixels according to the statistical characteristics of the image. Based on this, a novel graph similarity loss is proposed. This loss describes the latent spatial differences between the sample image and the generated image, which helps the model to better capture global features. Experiments show that our method outperforms existing methods on non‐homogeneous textures.

In this paper, the issue of stability and stabilization for semi-Markov jump memristive neural networks (SMJMNNs) with stochastic quantized sampled-data control (QSDC) law is addressed. Firstly, a memristive neural network (MNN) model with mixed semi-Markov jump is established in the framework of the three independent Markov chains . Then, a stochastic QSDC scheme is proposed, in which semi-Markov jump parameters (SMJPs) in the gain matrices, quantization parameters and system connection weight matrices are different from each other. On the other hand, since signal transmission may be delayed, the influence of transmission delay is considered in the proposed stochastic QSDC scheme. Based on the above, a more general weak infinitesimal operator about the three semi-Markov processes (SMPs) is first given to deal with stochastic Lyapunov functionals (LFs). To reduce the conservatism of the obtained results, stochastic LFs about the two-sided closed-loop functions are constructed in the framework of the sampling patterns ϑ ( t k ) to ϑ ( t ) and ϑ ( t ) to ϑ ( t k + 1 ) . Then, based on the novel constructed LFs, stochastic stability criterion for SMJMNNs is established by combining with stochastic QSDC law. Finally, the numerical simulation examples are provided to verify the validity and less conservatism of the obtained theoretical results.

The application of neural network models to solve combinatorial optimization has recently drawn much attention and shown promising results in dealing with similar problems, like Travelling Salesman Problem. The neural network allows to learn solutions based on given problem instances, using reinforcement learning or supervised learning. In this paper, we present a novel end-to-end method to solve routing problems. In specific, we propose a gated cosine-based attention model (GCAM) to train policies, which accelerates the training process and the convergence of policy. Extensive experiments on different scale of routing problems show that the proposed method can achieve faster convergence of the training process than the state-of-the-art deep learning models while achieving solutions of the same quality.

This paper focuses on the mult-layer unmanned aerial vehicles (UAVs) formation keeping control issue based on multi-agent bifurcation consensus. Taking the two-layer formation as an example, due to delays in receiving and responding to signals, the intra-layer and inter-layer control schemes under the dynamic properties need to be considered. Firstly, the UAV heading autopilot is designed to fully consider the control of the angle of attack and yaw angle, which is utilized to deal with the translational and angular motion of UAV. Based on the heading autopilot, the stability and Hopf bifurcation conditions of UAVs heading dynamic system are discussed to get real-time state information and generate control input. Secondly, the leader-following formation combined with the virtual control structure to obtain a virtual hierarchical control strategy, and then a bifurcation consensus criterion. The results shown that when UAVs heading control system achieves bifurcation consensus. The formation maintains the shape while showing interesting periodic oscillation motion behavior. Finally, a numerical example is presented to demonstrate the effectiveness of the presented control strategy. Particularly, visualization simulation using 3ds Max software makes the results more intuitive and flexible.

Exploring the stability of delta fractional order systems is essential for them to be used properly in various applications. Since the existing researches usually focused on the system with$\alpha \in (0,1)$, it is natural to ponder a parallel case with extra order. Motivated by this need, this study addresses the stability of delta delay fractional order systems with$\alpha \in (1,2)$systematically. The main difficulties lie in the approximation of stable region and the derivation of the relevant linear matrix inequalities (LMI) conditions caused by the complicated stable region of the suggested system. Firstly, a novel clustering region is constructed and it is proved that such a region is the subset of the considered stable region. Besides, the scheme on how to construct alternative approximation regions is discussed tentatively. Secondly, the stability conditions are formulated in terms of LMIs, which are sufficient and necessary to evaluate all the eigenvalues of system matrix locating at the approximation region. Thirdly, the complex decision matrices are replaced by the real ones and the formulation is therefore more tractable. Finally, the validity and applicability of the proposed approaches are demonstrated by simulation study.

In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems presented by the Takagi–Sugeno fuzzy model. The problem of nonfragile control is converted into solving the zero-sum games, where the control input and uncertain disturbance input can be regarded as two rival players. Based on the RL architecture, an offline parallel output feedback tracking learning algorithm is first designed to solve fuzzy stochastic coupled algebraic Riccati equations for Markov jump fuzzy systems. Furthermore, to overcome the requirement of a precise system information and transition probability, an online parallel integral RL-based algorithm is designed. Besides, the tracking object is achieved and the stochastically asymptotic stability, and expected$\mathcal {H}_{\infty }$performance for considered systems is ensured via the Lyapunov stability theory and stochastic analysis method. Furthermore, the effectiveness of the proposed control algorithm is verified by a robot arm system.

A fuzzy-neural-approximation-based pseudo nonaffine control protocol is proposed for waverider vehicles (WVs), which is capable of guaranteeing tracking errors with desired prescribed performance and rejecting the obstacle of fragility inherent to the traditional prescribed performance control (PPC). The pseudo control is defined to approximate the nonaffine dynamics of WVs, while there is no need of model affinization. Furthermore, fuzzy neural approximators are combined with the adaptive compensation strategy to resist both system uncertainties and external disturbances. Especially, a new type of nonfragile prescribed performance, being able to self-adjust its prescribed funnel, is proposed to remedy the fragility defect associated with the existing PPC. Finally, the realizability of the spurred prescribed performance is proved via stability proof, and the superiority of the addressed design is tested by compared simulations.

This study investigates an outlier-resistant state estimation problem for singularly perturbed complex networks (SPCNs) with sojourn probabilities and randomly occurring coupling strengths. Aiming at better describing the dynamic behavior of the network topology for SPCNs, a novel switching law associated with the time-varying sojourn probabilities is developed, and the variation of sojourn probabilities is arranged by a high-level deterministic switching signal. Meanwhile, a sequence of mode-dependent variables is employed to describe the randomly occurring coupling strength. Subsequently, to alleviate the side effects from possible measurement outliers, a dynamic saturation function-based state estimator is designed, whose saturation level is adaptively varying based on previous estimation errors. In virtue of Lyapunov theory and mode-dependent average dwell-time strategy, it can be verified that the resulting dynamics is stochastic$\mathcal {H}_{\infty }$finite-time bounded. To this end, a simulation example is presented to show the validity of the proposed estimator design method.

This article is concerned with the bipartite synchronization problem of coupled switching neural networks with cooperative–competitive interactions and reaction–diffusion terms. Different from the existing literature, the networked systems under investigation possess the relationship of cooperation and competition among nodes. Notably, the switching topology is described by a signed graph subject to the Markov jump process with the coexistence of positive and negative interaction weights. Specifically, a positive weight indicates an alliance relationship between two nodes and a negative one shows an adversary relationship. This article aims to design a bipartite synchronization controller for the aforementioned networks with the switching topology such that a prescribed$\mathcal {H}_{\infty }$bipartite synchronization is satisfied. Then, some sufficient criteria to ensure the stochastic stability of bipartite synchronization error systems are established in view of an appropriate Lyapunov function. Finally, two simulation examples are presented to verify the validity of the proposed bipartite synchronization control method.

This article studies the finite-time (FT) convergence of a fast primal-dual gradient dynamics (PDGD), called FT-PDGD, for solving constrained optimization with general constraints and cost functions. Based on the nonsmooth analysis and augmented Lagrangian function, sufficient conditions are established for FT-PDGD to enable the realization of primal-dual optimization in FT. A specific class of nonsmooth sign-preserving functions is defined and analyzed for ensuring FT stability. Particularly, the matrix of linear equations is not required to have a full-row rank and the cost function is not necessary to be strictly convex. By introducing auxiliary variables for general linear inequality constraints, reduced sufficient conditions are further derived for the optimization with linear equality and inequality constraints after transformation. In addition, by the nonsmooth analysis, the switching dynamics evolved in both primal and dual variables are carefully investigated and the upper bound on the convergence time is explicitly provided. Moreover, as applications of FT-PDGD, several FT convergent distributed algorithms are designed to solve distributed optimization with separated and coupled linear equations, respectively. Finally, two case studies are conducted to show the performance of the proposed algorithms.

The consensus tracking problem means that a group of followers tracks the desired trajectory with local communication. In this article, partial components of cluster consensus have been considered. In this scenario, the$p$components of the followers in different clusters track the leader at different lag times, while$p$components of each agent in the same cluster reach a consensus, which is called$p$components of cluster-lag (PCCL) consensus. By using a seminorm$\|x_{i}\|_{2,p}$and a Lyapunov–Krasovskii functional, PCCL consensus for second-order multiagent systems with homogeneous nonlinear systems on cooperative–competitive networks has been considered. For the case that the communication network graph is undirected, a decentralized adaptive controller, which is based on the exchanged neighbors’ information from the same cluster, is designed such that all the agents reach PCCL consensus. For the directed graph case, an adaptive protocol based on the intracoupling strength is constructed for each cluster to achieve PCCL consensus. Finally, two simulation examples are illustrated to show the effectiveness of the proposed control protocols.

In this article, a novel hybrid reinforcement$Q$-learning control method is proposed to solve the adaptive fuzzy$H_{\infty }$control problem of discrete-time nonlinear Markov jump systems based on the Takagi–Sugeno fuzzy model. First, the core problem of adaptive fuzzy$H_{\infty }$control is converted to solving fuzzy game coupled algebraic Riccati equation, which can hardly be solved by mathematical methods directly. To solve this problem, an offline parallel hybrid learning algorithm is first designed, where system dynamics should be known as a prior. Furthermore, an online parallel$Q$-learning hybrid learning algorithm is developed. The main characteristics of the proposed online hybrid learning algorithms are threefold: 1) system dynamics are avoided during the learning process; 2) compared with the policy iteration method, the restriction of the initial stable control policy is removed; and 3) compared with the value iteration method, a faster convergence rate can be obtained. Finally, we provide a tunnel diode circuit system model to validate the effectiveness of the present learning algorithm.

The usage of social media around the world is ever-increasing. Social media statistics from 2019 show that there are 3.5 billion social media users worldwide. However, the existence of community structure renders the network vulnerable to attacks and large-scale losses. How does one comprehensively consider the multiple information sources and effectively evaluate the vulnerability of the community? To answer this question, we design a gravity-based community vulnerability evaluation (GBCVE) model for multiple information considerations. Specifically, we construct the community network by the Jensen-Shannon divergence and log-sigmoid transition function to show the relationship between communities. The number of edges inside community and outside of each community, as well as the gravity index are the three important factors used in this model for evaluating the community vulnerability. These three factors correspond to the interior information of the community, small-scale interaction relationship, and large-scale interaction relationship, respectively. A fuzzy ranking algorithm is then used to describe the vulnerability relationship between different communities, and the sensitivity of different weighting parameters is then analyzed by Sobol' indices. We validate and demonstrate the applicability of our proposed community vulnerability evaluation method via three real-world complex network test examples. Our proposed model can be applied to find vulnerable components in a network to mitigate the influence of public opinions or natural disasters in real time. The community vulnerability evaluation results from our proposed model are expected to shed light on other properties of communities within social networks and have real-world applications across network science.

It is challenging to deal with the relationship between the triggering interval and the dwell time when mode-pendent event-triggered control is considered for switched systems, especially complete actuator faults occur in some subsystems (or modes). Therefore, this article considers the exponential stabilization almost surely (ES a.s.) for a class of Takagi–Sugeno fuzzy systems with delay and stochastic switching by designing a novel mode-dependent event-triggered output control. It is assumed that only the measured output can be utilized in designing the controller and complete actuator faults occur in some modes. Periodic sampling is utilized to avoid the Zeno phenomenon. Combining the sampling interval with delay-partitioning method, a fuzzy Lyapunov–Krasovskii functional (LKF) is designed on the basis of the derivative of the membership functions to obtain less conservative results. Especially, the increment coefficient of the multiple LKF at switching instant is smaller than one. The maximum feasible value of the sampling interval is derived and the control gains are designed by solving linear matrix inequalities. Our results show that the ES a.s. is ensured though some modes are not controlled. Numerical simulations demonstrate the merits of the theoretical analysis. It is significant to find by numerical simulations that the time delay cannot be divided into too small values though the delay-partitioning method is effective in reducing the conservativeness.

This paper focuses on the fuzzy control problem for a class of semi-Markov jump singularly perturbed nonlinear systems with actuator saturation. Nonlinearities in the underlying systems are tackled with the Takagi-Sugeno fuzzy model. As distinct from the previous achievements, the case that the semi- Markov kernel with partially known information is taken into account such that the underlying systems can be extended to a wider scope. Given that actuator saturation is ubiquitous in engineering systems, the convex hull method is embraced to address this circumstance. To reduce the conservativeness of the obtained results, on the basis of Lyapunov stability theory and LaSalle's invariance principle, some criteria related to the sojourn time are presented. Next, the mean-square exponential stability of the underlying system is obtained based on the derived results. Finally, further experiment to verify the feasibility of the proposed methodology is given by a tunnel diode circuit model.

This article investigates the exponential synchronization of fuzzy coupled reaction-diffusion neural networks (RDNNs) under hybrid random cyberattacks. To efficaciously tolerate the cyberattacks and guarantee the expected performance for the proposed systems, a fuzzy-regulation-dependent adaptive spatiotemporal security sampled-data-based event-triggered control scheme (SDBETCS) is first introduced according to distinct fuzzy regulations. In light of the current and latest sampling signals, the threshold parameters can be timely and flexibly updated and the associated adaptive spatiotemporal SDBETCSs can be adaptively regulated for different fuzzy rules. In comparison with the conventional fuzzy SDBETCSs, the designed fuzzy adaptive spatiotemporal SDBETCS can not only reduce the event-triggering frequency but also effectively conserve more finite network communication resources. Through considering a discontinuous Lyapunov functional, a new exponential synchronization criterion is provided for fuzzy coupled RDNNs. Furthermore, a more general fuzzy adaptive spatiotemporal SDBETCS with time-dependent and continuous threshold function is presented to compare with the traditional fuzzy SDBETCS. Finally, demonstrative examples are given to verify the validity and feasibility of the theoretical analysis results and illustrate its potential application in image secure communication.

In this article, a specified-time event-triggered fuzzy adaptive control algorithm is developed to solve the consensus tracking control problem for stochastic high-order nonlinear multiagent networks, which is intrinsically challenging due to the existence of stochasticity and high-order (positive odd integers greater than one) terms. More precisely, a novel specified-time performance function is incorporated into the time-varying high-order tan-type barrier Lyapunov function to guarantee that the tracking errors remain under time-varying constraints within specified time. Combining fuzzy logic systems with the adding on power integrator technique, an adaptive approximation policy is introduced to handle the system uncertainties. Moreover, a new switching threshold event-triggered mechanism is devised to determine the control signals updating instants, which reduces the transmission and computation burden, while resizing the triggering threshold in real time. The Zeno phenomenon is excluded by guaranteeing that the triggering intervals is lower bounded by a positive constant. Two simulation examples are provided to demonstrate the effectiveness of the designed algorithm.

Medical image fusion technology is an essential component of computer-aided diagnosis, which aims to extract useful cross-modality cues from raw signals to generate high-quality fused images. Many advanced methods focus on designing fusion rules, but there is still room for improvement in cross-modal information extraction. To this end, we propose a novel encoder-decoder architecture with three technical novelties. First, we divide the medical images into two attributes, namely pixel intensity distribution attributes and texture attributes, and thus design two self-reconstruction tasks to mine as many specific features as possible. Second, we propose a hybrid network combining a CNN and a transformer module to model both long-range and short-range dependencies. Moreover, we construct a self-adaptive weight fusion rule that automatically measures salient features. Extensive experiments on a public medical image dataset and other multimodal datasets show that the proposed method achieves satisfactory performance.

Neural network-based models have been implemented to predict various health indicators of asphalt pavement using pavement historical detection data. Unfortunately, their accuracy and reliability are not acceptable owing to their shallow architecture. To solve the issue, this study proposed an improved Transformer network to predict asphalt pavement health, called the Transformer with forward and reversed time series (Transformer FRTS). In terms of the input data, Transformer FRTS uses a new data form, so-called the random difference time series, to reduce the time dependency of the network prediction. In terms of the network architecture, the proposed network uses its encoder and decoder to obtain the data association from the forward and reverse time series. In addition, Transformer FRTS uses a post-processing decision criterion to improve the accuracy and reliability of prediction. The numerical experiment using the detection data from RIOHTrack full-scale track demonstrates that the proposed network has state-of-the-practice performance in asphalt pavement health prediction.

The dynamical study of continuous-/discontinuous-time fractional-order neural networks (FONNs) has been thoroughly explored, and several publications have been made available. This study is designed to give an exhaustive review of the dynamical studies of multidimensional FONNs in continuous/discontinuous time, including Hopfield NNs (HNNs), Cohen-Grossberg NNs, and bidirectional associative memory NNs, and similar models are considered in real ( [Formula: see text]), complex ( [Formula: see text]), quaternion ( [Formula: see text]), and octonion ( [Formula: see text]) fields. Since, in practice, delays are unavoidable, theoretical findings from multidimensional FONNs with various types of delays are thoroughly evaluated. Some required and adequate stability and synchronization requirements are also mentioned for fractional-order NNs without delays.

This article investigates the asynchronous proportional-integral observer (PIO) design issue for singularly perturbed complex networks (SPCNs) subject to cyberattacks. The switching topology of SPCNs is regulated by a nonhomogeneous Markov switching process, whose time-varying transition probabilities are polytope structured. Besides, the multiple scalar Winner processes are applied to character the stochastic disturbances of the inner linking strengths. Two mutually independent Bernoulli stochastic variables are exploited to characterize the random occurrences of cyberattacks. In a practical viewpoint, by resorting to the hidden nonhomogeneous Markov model, an asynchronous PIO is formulated. Under such a framework, by applying the Lyapunov theory, sufficient conditions are established such that the augmented dynamic is mean-square exponentially ultimately bounded. Finally, the effectiveness of the theoretical results is verified by two numerical simulations.

This article studies the asynchronous fault detection filter problem for discrete-time memristive neural networks with a stochastic communication protocol (SCP) and denial-of-service attacks. Aiming at alleviating the occurrence of network-induced phenomena, a dwell-time-based SCP is scheduled to coordinate the packet transmission between sensors and filter, whose deterministic switching signal arranges the proper feedback switching information among the homogeneous Markov processes (HMPs) for different scenarios. A variable obeying the Bernoulli distribution is proposed to characterize the randomly occurring denial-of-service attacks, in which the attack rate is uncertain. More specifically, both dwell-time-based SCP and denial-of-service attacks are modeled by means of compensation strategy. In light of the mode mismatches between data transmission and filter, a hidden Markov model (HMM) is adopted to describe the asynchronous fault detection filter. Consequently, sufficient conditions of stochastic stability of memristive neural networks are devised with the assistance of Lyapunov theory. In the end, a numerical example is applied to show the effectiveness of the theoretical method.

This article investigates the intermittent event-triggered optimal leader-following consensus for nonlinear multi-agent systems (MASs) utilizing the actor-critic algorithm. First, we propose a novel distributed intermittent event-triggered control strategy, and a sufficient criterion is obtained to guarantee the leader-following consensus of MASs by establishing a novel piecewise differential inequality. Next, the intermittent event-triggered optimal control strategy is delicately given. Remarkably, the optimality of MASs is proven based on policy iteration and the convergence of the closed-loop system is also proved based on the Lyapunov stability theory. Then, the intermittent event-triggered approximate optimal control strategy is designed via an actor-critic network whose weights are only updated at the trigger instants. Furthermore, the Zeno behavior can be excluded in this article. Finally, two simulation examples further verify the effectiveness of the proposed scheme.

This article studies the local exponential synchronization synthesis problem of directed Lur'e networks with coupling time-varying delay under the distributed impulsive control subject to actuator saturation. First, by utilizing proof by contradiction, impulsive comparison principle, and latest improved convex hull representation of saturation function, some delay-independent sufficient criteria for local exponential synchronization are presented in the form of bilinear matrix inequalities. Meanwhile, a novel method with less conservatism is developed to estimate the domain of attraction, which is radically different from the traditional method by means of contractive invariant set. Second, optimization problems constrained by the transformed linear matrix inequalities are established to acquire the maximum estimates of both the domain of attraction and average impulsive interval (AII), which are conveniently solved by the YALMIP toolbox in MATLAB software. Finally, a numerical simulation is rendered to demonstrate the effectiveness and advantages of the proposed theoretical results.

This work explores the$H_{\infty }$synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the$H_{\infty }$performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.

bipartite synchronization issue is studied for a class of discrete-time coupled switched neural networks with antagonistic interactions via a distributed dynamic event-triggered control scheme. Essentially different from most current literature, the topology switching of the investigated signed graph is governed by a double-layer switching signal, which integrates a flexible deterministic switching regularity, the persistent dwell-time switching, into a Markov chain to represent the variation of transition probability. Considering the coexistence of cooperative and antagonistic interactions among nodes, the bipartite synchronization of which the dynamics of nodes converge to values with the same modulus but the opposite signs is explored. A distributed control strategy based on the dynamic event-triggered mechanism is utilized to achieve this goal. Under this circumstance, the information update of the controller presents an aperiodic manner, and the frequency of data transmission can be reduced extensively. Thereafter, by constructing a novel Lyapunov function depending on both the switching signal and the internal dynamic nonnegative variable of the triggering mechanism, the exponential stability of bipartite synchronization error systems in the mean-square sense is analyzed. Finally, two simulation examples are provided to illustrate the effectiveness of the derived results.

This article focuses on stability analysis of delayed reaction-diffusion neural-network models with hybrid impulses based on the vector Lyapunov function. First, several properties of a vector Halanay-type inequality are given to be the key ingredient for the stability analysis. Then, the Krasovskii-type theorems are established for sufficient conditions of exponential stability, which removes the common threshold of impulses in each neuron subsystem at every impulse time. It shows that the stability of neural networks can be retained with hybrid impulses involved in neural networks, and the synchronization of neural networks can be achieved by designing an impulsive controller, which allows the existence of impulsive perturbation in some nodes and time. Finally, the effectiveness of theoretical results is verified by numerical examples with a successful application to image encryption.

Passenger-flow anomaly detection and prediction are essential tasks for intelligent operation of the metro system. Accurate passenger-flow representation is the foundation of them. However, spatiotemporal dependencies, complex dynamic changes, and anomalies of passenger-flow data bring great challenges to data representation. Taking advantage of the time-varying characteristics of data, we propose a novel passenger-flow representation model based on low-rank dynamic mode decomposition (DMD), which also integrates the global low-rank nature and sparsity to explore the spatiotemporal consistency of data and depict abrupt data, respectively. The model can detect anomalies and predict short-term passenger flow conveniently and flexibly. For anomaly detection, we further introduce a strong temporal Toeplitz regularization to characterize the temporal periodic change of data, so as to more accurately detect anomalies. We conduct experiments with smart card transaction data from the Beijing metro system to assess the performance of the model in two use cases. In terms of anomaly detection, the experimental results demonstrate that our method can detect anomalies efficiently, especially for time sequence anomalies. As for short-term prediction, our model is superior to other methods in most cases.

This technical note proposes a decentralized-partial-consensus optimization (DPCO) problem with inequality constraints. The partial-consensus matrix originating from the Laplacian matrix is constructed to tackle the partial-consensus constraints. A continuous-time algorithm based on multiple interconnected recurrent neural networks (RNNs) is derived to solve the optimization problem. In addition, based on nonsmooth analysis and Lyapunov theory, the convergence of continuous-time algorithm is further proved. Finally, several examples demonstrate the effectiveness of main results.

This article presents a decentralized learning control method for a class of partially unknown nonlinear systems with asymmetric control input constraints and mismatched interconnections via a novel dynamic event-triggering condition. By employing an integral reinforcement learning strategy, the system drift dynamics can be avoided in the learning process. Meanwhile, a critic neural network is designed to obtain the approximated value function and tuned by using the gradient descent approach. Furthermore, a novel dynamic event-triggering condition is designed to determine the occurrence of an event by introducing a dynamic variable. By using the Lyapunov theory, all signals in the closed-loop system are proved to be uniformly ultimately bounded. Finally, we present a nonlinear interconnected system and an interconnected power system to verify the effectiveness of the proposed method.

This article considers a distributed adaptive control scheme for a class of second-order leader-following multiagent systems with only position information as output. An auxiliary network for the agent network is modeled to estimate the unmeasurable velocity information and make the output-based distributed adaptive control protocol effective. Under the distributed adaptive control scheme based on the auxiliary network model, several distributed synchronization criteria are established, and the corresponding convergence analysis is provided based on the stability theory. Finally, several simulation examples are provided to conform to the presented criteria.

This article studies the minimal reconstructibility of Boolean control networks (BCNs) based on the semi-tensor product (STP). Two effective criteria for the reconstructibility of BCNs under two definitions are proposed by using weak control invariant subset (WCIS) and strong control invariant subset (SCIS). By injecting new measurements, a minimal reconstructibility problem (MRP) is established for achieving reconstructibility. Based on constructing an index matrix, the solution of the MRP is transformed into the solution of the equation. Finally, a biological example is given to illustrate the theoretical results and further emphasize the inequivalence of the two definitions.

This article investigates the exponential synchronization issue for networks of variable-order fractional piecewise-smooth systems with short memory, where each node is modeled to satisfy$\sigma $-QUAD condition, and possess the incommensurate order. First, the generalized Caputo variable-order fractional derivative (VOFD) operator is defined, and some properties and lemmas with respect to VOFD are developed. Second, the dynamic event-triggered control mechanism is designed, where the additional internal dynamic variable is subject to the nonlinear variable-order fractional dynamical system with incommensurate order. Third, by exploiting the Lyapunov stability theory and some auxiliary inequality techniques, the exponential synchronization criteria are established in terms of linear matrix inequalities (LMIs) under the designed control scheme. In addition, the nonexistence of Zeno behavior is proved by contradiction. Finally, a numerical example is given to illustrate the effectiveness of the main results.

This article concentrates on optimal tracking control for a class of nonlinear networked systems subjecting to limited network bandwidth and unmatched disturbance. Given the models of the control and reference systems, the considered optimal tracking control issue is initially formulated as a minimax optimization problem. Then, with the introduction of an event-triggered mechanism used for saving bandwidth, the formulated problem is transformed into solving an event-based Hamilton-Jacobi-Isaacs (HJI) equation by recurring to the Bellman optimality theory. Based on the HJI equation, we demonstrate that the stability of the concerned system in the sense of uniformly ultimately bounded (UUB) can be guaranteed with the envisioned optimal control and worst disturbance policies. Here, the disturbance policy can be varied periodically while the control policy can only be updated at event-triggering instants, which differs from the existed researches. Furthermore, we propose a reinforcement learning (RL)-based algorithm to handle the constructed HJI equation and thus settle the studied tracking control problem. The effectiveness of the algorithm is finally validated by both theoretical analysis and simulations.

This article considers the cluster synchronization of coupled genetic oscillator networks (GONs) with proportional delay via the event-triggered pinning impulsive control (ETPIC). First, a novel Lyapunov-dependent event-triggered rule for impulsive functional differential equation that includes proportional delay is proposed by means of a new auxiliary function and the improved Lyapunov–Razumikhin method, which is different from the previous results. And then, a Lyapunov-dependent ETPIC rule is first designed, which integrates the pinning control with the established event-triggered impulsive control. On this basis, some new cluster synchronization criteria for coupled GONs with proportional delay are derived in the light of linear matrix inequalities. Note that the triggering impulsive instant is determined by the designed ETPIC rule, and only a fraction of nodes of each cluster are controlled at each triggering instant according to the designed algorithm. Finally, a numerical example is provided to show the effectiveness of the proposed approach.

This article concentrates on the discrete-time sliding mode control (DTSMC) problem for uncertain networked semi-Markovian switching systems (S-MSSs) under random denial-of-service (DoS) attacks. The semi-Markovian kernel (SMK) approach is utilized such that the switching among different modes is mutually governed by the transition probability and the sojourn-time distribution function. Considering randomly occurring DoS attacks, a sliding mode function related to the attack probability is constructed to analyze the impact of malicious attacks. Then, sufficient conditions under the equivalent DTSMC law are derived in view of the$\sigma $-error mean square stability criterion. Furthermore, the synthesis problem of the proposed DTSMC law ensures that the resulting closed-loop system dynamics can be driven onto the prespecified sliding region within a limited time. Finally, an electronic throttle control system is shown to validate the proposed algorithm.

In this study, a framework for processing gradient algorithms is proposed in accordance with nabla fractional-order system theory. Unlike most of the literature, the gradient algorithm is initially recast into a nabla fractional-order dynamic system. To be specific, the algorithm is designed using control theory and analyzed using the Lyapunov theory, which is a more general and effective way to render high-performance algorithm. Three types of algorithms are built in this study, i.e., the asymptotic convergence case, the finite-time convergence case, and the fixed-time convergence case. Finally, a comprehensive simulation study is conducted verifying the correctness, usefulness, and practicality of the framework.

This article is centered on the formulation of a refined adaptive sampled-data-based event-triggering control (ASDBETC) scheme for the synchronization of reaction–diffusion complex-valued neural networks (RDCVNNs) with probabilistic time-varying delays (TVDs). A refined ASDBETC mechanism is first proposed in a hierarchy structure, which differs from some conventional sampled-data-based event-triggering control mechanisms with preordained invariable threshold. The refined ASDBETC mechanism can adaptively adjust the orientation and frequency of event-triggered threshold parameters via the variational tendency of states and the corresponding Euclidean distance between states. Therefore, an intact bidirectional regulative mechanism that is sensitive to the changes of state is legitimately established to provide additional flexibility, which is conducive to better compromise between communication resources and control performance. Through considering the effect of uncertainties, the random TVDs belonging to two different intervals by a probabilistic form are introduced. Then, by leveraging a novel time-related Lyapunov–Krasovskii functional (LKF) that contains the more realistic sampled behaviors on the entire sampled interval, new synchronization conditions and controller design method are derived for RDCVNNs. Finally, the advantages of the refined ASDBETC strategy, the availability, and practicability of the developed theories are corroborated via two simulation examples.

This article studies the coordination problem for second-order multiagent systems with the presence of antagonistic information among participating agents, while suffering from the lack of the velocity information. To this end, a coordination algorithm, involving an external variable that enables us to circumvent the velocity information, is proposed, of which nonzero scaling parameters are used to characterize whether the underlying interactions are antagonistic or not. A distinctive feature of the devised coordination algorithm, apart from the interest of its own, is that there does not necessitate the signed graph and structurally balanced theories that are commonly adopted in the literature. We show that the proposed setup is amenable for coordination of the agents, with the help of some newly introduced transformation relaxing the dependence of the weighted states of agents or some reference signals that are global information, and are commonly used in most of the existing studies where consensus problems are recast into the stability counterparts of the corresponded error systems. We also show that weighted gains used to assure the underlying Laplacian “like” matrix acting a normal Laplacian matrix, scaling parameters, and communication topology are of great importance for the investigated coordination problem, giving rise to that there does not enjoy any trivial equivalence between the devised setup and these in the literature. Finally, two numerical examples, including a multirobot system, are carried out to support the developed method.

The rapid development of community detection algorithms, while serving users in social networks, also brings about certain privacy problems. In this work, we study community deception, which aims to counter malicious community detection attacks by imperceptibly modifying a small part of the connections. However, it is computationally challenging to find an optimal edge set since it is an NP-hard problem. To address this issue, we propose a self-adaptive evolutionary deception (SAEP) framework. In SAEP, a novel fitness function that is able to capture local and global community change is being proposed. SAEP also provides a well-designed initialization mechanism to reduce the size of the solution space. In addition, we assign an indicator to each gene to reflect its strength within the chromosome that it belongs to, thereby a set of self-adaptive operations can be defined to enhance the algorithm’s stability and efficacy. Furthermore, we define a new “edge distance” to conserve the limited modification resource on the graph. In the experiment, the proposed method is tested against different community detection methods using various real-world datasets, and the experimental results demonstrate that SAEP improves significantly over state-of-the-art approaches in terms of effectiveness.

This work concerns the event-based asynchronous filtering problem for T-S fuzzy nonhomogeneous Markov switching systems with variable packet dropouts. The discrete-time nonhomogeneous Markov process is adopted to depict mode switching among subsystems, in which the time-varying transition probabilities are characterized by a polytope structure. The variable packet dropouts are developed to describe the randomly occurring packet dropouts, where the arriving rate remains variable and uncertain. Aiming to save the limited network bandwidth, event-triggered strategy and quantization scheme are presented. By establishing the fuzzy-rule-dependent Lyapunov functional and applying a hidden Markov model policy, sufficient criteria are gained and asynchronous filters are designed by solving linear matrix inequalities (LMIs). Finally, the applicability of the proposed filtering strategy is verified by an inverted pendulum model.

Images and videos shot in low light are often accompanied by severe image degradation, such as color noise, chromatic aberrations and loss of details. Most existing convolutional neural network (CNN)-based low-light enhancement methods focus on decomposing the image into illumination and reflection parts via the Retinex model, but these methods often fail to adequately consider controlling noise during enhancement and perform poorly in the face of complex lighting environments. In this letter, we propose a powerful Vision Transformer-based Generative Adversarial Network (Transformer-GAN) for enhancing low-light images. Transformer-GAN consists of two subnets as follows: (1) the feature extraction is achieved by an iterative multi-branch network in the feature extraction subnet, and (2) the enhancement is completed in the image reconstruction subnet. The innovative core works are multi-head multi-covariance self-attention (MHMCA) and Light feature-forward module structures (LFFM) in Transformer-GAN. Experiments demonstrate that our method outperforms state-of-the-art low-light enhancement methods on popular low-light datasets.

This paper focuses on the synchronization control issue of networked systems. Due to the imperfect network environment, continuous or precise transmission is impractical, and intermittent communication scheme under state quantization needs to be considered. First, an easy-to-implement dynamic quantizer, which possesses finite quantization level, is designed to deal with the imprecise information sharing. Next, based on the designed quantizer, a dynamic estimator is introduced to estimate the real-time state information and generate control inputs. An event-based communication scheme is utilized to ensure that the estimate error does not exceed a certain threshold. Then, detailed design of the dynamically quantized controller is given to achieve exact synchronization, and corresponding distributed design is also provided to improve the feasibility. Moreover, some results for practical synchronization are derived. Finally, the validity of our theoretical results is illustrated by two numerical examples.

Demand response (DR) of power grids services an increasing key component of smart grids. This article is devoted to the DR active power control for smart buildings based on a cloud platform for security interconnection with power grids. The objective of the DR control is to cope with the intermittent renewable power injection and power fluctuation of the tie-line between the building and the distribution grid. The renewable energy power includes photovoltaic panel and on-site wind power generators on the building roof, and the flexible loads includes electric vehicles (EVs) connected to the building, and thermostatically controlled loads (TCLs) in the building. We propose two fair and flexible indexes, comfortable levels for TCLs and the charging priority for EVs, and design two kinds of DR control algorithm, i.e., centralized load reduction control algorithm and distributed load following control algorithm, to coordinate the power balance of the building while maintain the grid exchange power is distributed in a probabilistic security controllable interval such that the building turns out to be a grid-friendly building. The case study is tested on a smart building with random renewable power injection and flowing EVs, civil electric water heaters (EWHs) thermostatically controlled loads and the simulations results shows the effectiveness of the proposed DR control algorithms.

This article concentrates on the synchronization of discrete-time persistent dwell-time (PDT) switched bidirectional associative memory inertial neural networks with time-varying delays. Through the use of the switched system theory related to the PDT, the convex optimization technique together with some straightforward decoupling methods, an appropriate mode-dependent controller with nonfragility is developed to acclimatize itself to some practical circumstances. Simultaneously, sufficient conditions of ensuring the$\mathcal {H}_{\infty }$performance and exponential stability for the resulting switched synchronization error system are derived. Finally, a numerical example is utilized to show the validity of the model constructed and the influence of the PDT on the$\mathcal {H}_{\infty }$performance. In addition, an image encryption example is employed to show the potential application prospect of the investigated system.

In this article, we study the finite-time stabilization and the asymptotic stabilization with probability one of Markovian jump Boolean control networks (MJBCNs) by sampled-data state feedback controls (SDSFCs). Based on the semi-tensor product (STP), we introduce an augmented variable multiplied by the vector form of the switching signal and the state of MJBCN. We find that under SDSFC, the sequence of the states of the augmented variable at sampling instants satisfies the Markov property. Based on the convergences of the switching signal and the augmented variable, we obtain the sufficient and necessary criteria for the finite-time stabilization and the asymptotic stabilization of MJBCNs by SDSFCs, respectively. Moreover, for the two kinds of stabilization, the feedback matrices of SDSFCs are constructed, respectively. Finally, the obtained results are applied to an apoptosis network and a model of the lactose operon in the Escherichia Coli.

This work is concerned with the issue of finite-time filter design for a type of Takagi-Sugeno (T-S) fuzzy Markov switching system (MSSs) with deception attacks (DAs). In view of communication network security, the randomly occurring DAs are considered in the measurement output (MO), in which the malicious unknown but bounded signals are launched by the adversary. Notably, to characterize the fallibility of the communication links between the MO and the filter, the packet dropouts, DAs, and quantization effects are taken into account simultaneously, which signifies that the resulting system is much more applicable than the existing results. Meanwhile, to deal with the phenomenon of asynchronous switching, a hierarchical structure approach is adopted, which involves the existing nonsynchronous/synchronous strategy as special cases. By means of a fuzzy-basis-dependent Lyapunov strategy, sufficient criteria are formulated such that the resulting system is stochastic finite-time boundedness under randomly occurring DAs. Finally, a double-inverted pendulum model and a numerical example are provided to validate the feasibility of the attained method.

A new type of asymptotic stability for nonlinear hybrid neutral stochastic systems with constant delays was investigated recently, where the criteria depended on the delays’ sizes. Unfortunately, developed theory so far is not sufficient to deal with challenging problems of the decay rate, time-varying delays, and nonautonomous issues. These problems have not been tackled in the existing literature. Consequently, under the weak constraints, this article focuses on the general decay, including the exponential stability and the polynomial stability, for nonlinear nonautonomous hybrid neutral stochastic systems with time-varying delays by the approach of the multiple degenerate functionals. Moreover, this article derives the interesting assertions related to the general$H_{\infty }$stability and the polynomial growth at most.

This article identifies a new upper bound norm for the intervalized interconnection matrices pertaining to delayed dynamical neural networks under the parameter uncertainties. By formulating the appropriate Lyapunov functional and slope-bounded activation functions, the derived new upper bound norms provide new sufficient conditions corresponding to the equilibrium point of the globally asymptotic robust stability with respect to the delayed neural networks. The new upper bound norm also yields the optimized minimum results as compared with some existing methods. Numerical examples are given to demonstrate the effectiveness of the proposed results obtained through the new upper bound norm method.

In nature and human society, successive lag synchronization (SLS) is an important synchronization phenomenon. Compared with other synchronization patterns, the control theory of SLS is very lacking. To this end, we first introduce a complex dynamical network model with distributed delayed couplings, and design both the linear feedback pinning control and adaptive feedback pinning control to push SLS to the desired trajectories. Second, we obtain a series of sufficient conditions to achieve SLS to a desired trajectory with global stability. What is more, the control flow of SLS is given to show how to pick the pinned nodes accurately and set the feedback gains as well. Finally, since time-varying delay is common, we extend the constant time delay in SLS to be time varying. We find that the proposed pinning control schemes are still feasible if the coupling terms are appropriately adjusted. The theoretical results are verified on a neural network and the coupled Chua's circuits.

In this article, the problem of passivity and dissipativity analysis is investigated for a class of fractional-order quaternion-valued fuzzy memristive neural networks. Based on the famous nonlinear scalarizing function, a nonlinear scalarization method is developed, which can be employed to compare the "size" of two different quaternions. In this way, the convex closure proposed by the quaternion-valued connection weights is meaningful. By constructing proper Lyapunov functional, several improved passivity criteria and dissipativity conclusions are established, which can be checked efficiently by utilizing some standard mathematical calculations. Finally, the obtained results are validated by simulation examples.

This article aims to stabilize probabilistic Boolean networks (PBNs) via a novel pinning control strategy. In a PBN, the state evolution of each gene switches among a collection of candidate Boolean functions with preassigned probability distributions, which govern the activation frequency of each Boolean function. Due to the existence of stochasticity, the mode-independent pinning controller might be disabled. Thus, both mode-independent and mode-dependent pinning controller are required here. Moreover, a criterion is derived to determine whether mode-independent controllers are applicable while the pinned nodes are given. It is worth pointing out that this pinning control is based on the$n\times n$network structure rather than$2^{n} \times 2^{n}$state transition matrix. Therefore, compared with the existing results, this pinning control strategy is more practicable and has the ability to handle large-scale networks, especially sparsely connected networks. To demonstrate the effectiveness of the designed control scheme, a PBN that describes the mammalian cell-cycle encountering a mutated phenotype is discussed as a simulation.

This work investigates the consensus tracking problem for high-power nonlinear multiagent systems with partially unknown control directions. The main challenge of considering such dynamics lies in the fact that their linearized dynamics contain uncontrollable modes, making the standard backstepping technique fail; also, the presence of mixed unknown control directions (some being known and some being unknown) requires a piecewise Nussbaum function that exploits the a priori knowledge of the known control directions. The piecewise Nussbaum function technique leaves some open problems, such as Can the technique handle multiagent dynamics beyond the standard backstepping procedure? and Can the technique handle more than one control direction for each agent? In this work, we propose a hybrid Nussbaum technique that can handle uncertain agents with high-power dynamics where the backstepping procedure fails, with nonsmooth behaviors (switching and quantization), and with multiple unknown control directions for each agent.

This article is concerned with the issue of quantized sliding-mode control (SMC) design methodology for nonlinear stochastic switching systems subject to semi-Markovian switching parameters, T-S fuzzy strategy, uncertainty, signal quantization, and nonlinearity. Compared with the previous literature, the quantized control input is first considered in studying T-S fuzzy stochastic switching systems with a semi-Markovian process. A mode-independent sliding surface is adopted to avoid the potential repetitive jumping effects. Then, by means of the Lyapunov function, stochastic stability criteria are proposed to be dependent of sojourn time for the corresponding sliding-mode dynamics. Furthermore, the fuzzy-model-based SMC law is proposed to ensure the finite-time reachability of the sliding-mode dynamics. Finally, an application example of a modified series dc motor model is provided to demonstrate the effectiveness of the theoretical findings.

This article focuses on the composite$\mathcal {H}_{\infty }$synchronization problem for jumping reaction–diffusion neural networks (NNs) with multiple kinds of disturbances. Due to the existence of disturbance effects, the performance of the aforementioned system would be degraded; therefore, improving the control performance of closed-loop NNs is the main goal of this article. Notably, for these disturbances, one of them can be described as a norm-bounded, and the other is generated by an exogenous model. In order to reject the above one kind of disturbance, a disturbance observer is developed. Furthermore, combining the disturbance observer approach and conventional state-feedback control scheme, a composite disturbance rejection controller is specifically designed to compensate for the influences of the disturbances. Then, some criteria are established based on the general Lyapunov stability theory, which can ensure that the synchronization error system is stochastically stable and satisfies a fixed$\mathcal {H}_{\infty } $performance level. A simulation example is finally presented to verify the availability of our developed method.

The time-triggered impulsive control of complex homogeneous dynamical networks has received wide attention due to its occasional occupation of the communication channels. This article is devoted to quasisynchronization of heterogeneous dynamical networks via event-triggered impulsive controls with less channel occupation. Two kinds of triggered mechanisms, that is, the centralized event-triggered mechanism in which the control is updated based upon the state information of all nodes, and the distributed event-triggered mechanism where the control is updated according to the state information of each node and its neighboring node, are proposed, respectively, such that the synchronization error between the heterogeneous dynamical networks and a virtual target is not more than a nonzero bound. What is more, the Zeno behavior is shown to be excluded. It is found that the combination method of the event-triggered control and the impulsive control, that is, the distributed event-triggered impulsive control has the advantage of low-energy consumption and takes up many fewer communication channels over the time-triggered impulsive control. Two numerical examples are conducted to illustrate the effectiveness of the proposed event-triggered impulsive controls.

In this article, a periodic self-triggered impulsive (PSTI) control scheme is proposed to achieve synchronization of neural networks (NNs). Two kinds of impulsive gains with constant and random values are considered, and the corresponding synchronization criteria are obtained based on tools from impulsive control, event-driven control theory, and stability analysis. The designed triggering protocol is simpler, easier to implement, and more flexible compared with some previously reported algorithms as the protocol combines the advantages of the periodic sampling and event-driven control. In addition, the chaotic synchronization of NNs via the presented PSTI sampling is further applied to encrypt images. Several examples are also utilized to illustrate the validity of the presented synchronization algorithm of NNs based on PSTI control and its potential applications in image processing.

This article focuses on a distributed optimization problem subject to partial-impact cost functions that relates to two decision variable vectors. To this end, two algorithms are presented with the aim of solving the considered optimization problem in a structure fashion and in a gradient fashion, respectively. Furthermore, a connection between the equilibrium of the induced algorithm and the involved optimization problem is established, with the aid of the tools from nonsmooth analysis and change of coordinate theorem. Two numerical examples with practical significance are given to demonstrate the efficiency of the designed algorithm.

This article is concerned with the problem of the$H_{\infty }$output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed$H_{\infty }$performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.

This article is concerned with the global Mittag-Leffler group consensus and group consensus in finite time for fractional multiagent systems (FMASs), where the inherent dynamics is modeled to be discontinuous, and subject to the local Hölder nonlinear growth in a neighborhood of continuous points. First, a fractional differential inequality on convex functions and a global convergence principle in finite time for absolutely continuous functions are developed, respectively. Second, two new distributed control protocols are designed to realize the consensus between the follower agents in each subgroup and respective leaders. In addition, under the fractional Filippov differential inclusion framework, by applying the Lur'e Postnikov-type convex Lyapunov functional approach and Clarke's nonsmooth analysis technique, some sufficient conditions with respect to the global Mittag-Leffler group consensus and group consensus in finite time are addressed in terms of linear matrix inequalities (LMIs), respectively. Moreover, the settling time for the group consensus in finite time is estimated accurately. Finally, two simulation examples are provided to illustrate the validity of the proposed scheme and theoretical results.

A large number of experiments have proved that the ring structure is a common phenomenon in neural networks. Nevertheless, a few works have been devoted to studying the neurodynamics of networks with only one ring. Little is known about the dynamics of neural networks with multiple rings. Consequently, the study of neural networks with multiring structure is of more practical significance. In this article, a class of high-dimensional neural networks with three rings and multiple delays is proposed. Such network has an asymmetric structure, which entails that each ring has a different number of neurons. Simultaneously, three rings share a common node. Selecting the time delay as the bifurcation parameter, the stability switches are ascertained and the sufficient condition of Hopf bifurcation is derived. It is further revealed that both the number of neurons in the ring and the total number of neurons have obvious influences on the stability and bifurcation of the neural network. Ultimately, some numerical simulations are given to illustrate our qualitative results and to underpin the discussion.

This article addresses the asynchronous control for singularly perturbed systems with nonhomogeneous Markov switching approximated by T–S fuzzy models. The transition probabilities of the nonhomogeneous Markov process are supposed to be time-varying and distinguished by of a polytopic set. As distinct from some reported works, to abate the effect of missing packets in unreliable communication network, a novel dropout compensation strategy is constructed, where the packet arriving rate is assumed to be uncertain. Meanwhile, to describe the asynchronizations of the dropout compensation strategy and the controller, the hidden Markov models are absorbed. By resorting to the fuzzy-rule-dependent and the parameter-dependent Lyapunov–Krasovskii functional, novel sufficient conditions of$\mathcal {H}_{\infty }$performance are formulated and the fuzzy-based asynchronous controller gains are realized. Finally, to testify the efficiency and applicability of the proposed results, a numerical example and practical tunnel diode circuit system are provided.

This article focuses on static output feedback control for fuzzy Markovian switching singularly perturbed systems (FMSSPSs) with deception attacks and asynchronous quantized measurement output. Different from the previous work, both the logarithmic quantizer and the static output feedback controller are dependent on the operation system; by means of hidden Markov models, their modes run asynchronously with that of FMSSPSs. Additionally, the deception attacks are guided by a Bernoulli variable, and nonlinear characteristics are modeled by the Takagi–Sugeno fuzzy model. By resorting to a mode-dependent Lyapunov functional, several criteria are acquired and strictly$(\mathscr {Q},\mathscr {S},\mathscr {R})\text{--}\gamma$-dissipative of FMSSPSs can be ensured. Finally, a dc motor model is expressed to illustrate the effectiveness of the asynchronous control scheme.

In this article, an adaptive fuzzy predefined time and accuracy tracking control scheme is proposed for strict-feedback nonlinear systems. A sufficient condition that can be used to determine whether the tracking error converges to a predefined region within a predefined time is first derived based on the definition of predefined time stability. Under the proposed stability criterion, an adaptive fuzzy controller is developed by applying the backstepping technique, and the stability time and convergence accuracy of the tracking error can be set previously, which facilitates the engineering application with requirements for convergence time and accuracy. Especially, to reduce chattering and decrease energy consumptions on the premise of ensuring the stability of the system, an inequality is introduced to construct a continuous term which can replace the sign function. Finally, a practical example is presented to illustrate the feasibility of main results.

This article investigates the problem of designing$\mathcal {H}_{\infty }$dynamic output feedback reliable controller for discrete-time Markov jump nonlinear systems with persistent dwell-time switched transition probabilities based on the Tagaki–Sugeno fuzzy model. The uncertainty of measurement output, which is assumed to occur randomly, and mode-dependent actuator faults are considered simultaneously. Moreover, the jumping property presented by system modes is described by the Markov chain of which transition probabilities are considered to be piecewise time-varying, and is described by adopting the more flexible persistent dwell-time switching rule. Based on the stochastic analysis approach and Lyapunov stability theory, some sufficient conditions are established to ensure the resulting closed-loop system being mean-square exponentially stable with the prescribed$\mathcal {H}_{\infty }$performance. Furthermore, the desired controller gains can be obtained through solving a convex optimization problem. Finally, the practicability and availability of the proposed control method are illustrated by a numerical example and a modified tunnel diode circuit model.

This article is concerned with exponential stabilization almost surely for a class of discrete-time switched T–S fuzzy systems (DTSTSFSs), in which each mode switches according to transition probability and evolves with mode-dependent average dwell time. Parallel distributed compensation (PDC) event-triggered dynamic output quantization (ETDOQ) control is considered for the DTSTSFSs, where event-triggered (ET) mechanism without Zeno behavior is designed, which is mode-dependent and does not restrict the dwell time of each mode. By using the mode-dependent Lyapunov function method, two sufficient conditions termed by linear matrix inequalities are provided. Different from most of existing results, no inequality is resorted in dealing with the uncertainties coming from the quantization. It is interesting that the controlled modes are not required to be stable. New algorithm is designed for the control gains of the ETDOQ controller and the parameters of the ET condition, which overcomes the mismatching difficulty of the parameters induced by the PDC. Numerical simulations demonstrate the merits of the theoretical analysis.

In this article, the finite-time synchronization of drive-response fuzzy inertial neural networks with delays is considered. Without applying the finite-time stability theorems and integral inequality approach, by using the maximum value approach of functions and designing two kinds of different controllers of time variablet, two criteria ensuring the finite-time synchronization for the drive-response fuzzy inertial neural networks are put forward. In this article, since the maximum value approach of functions is used and the controllers of time variable$t$are designed in studying the finite-time synchronization, our approach and results on the finite-time synchronization of neural networks are new.

A single infrared image or visible image for the same scene is usually insufficient to simultaneously reveal the infrared objects and the scene details. Thus, image fusion techniques play an important role in producing a single image from the images captured by infrared and visible sensors. In this paper, we propose a novel total variation (TV)-based fusion for infrared and visible images. In our model, a weighted fidelity term is employed to fuse both the infrared objects in the infrared image and the salient scenes in the visible image. To this end, a weight estimation method is developed based on the global luminance contrast-based saliency. Also, to overcome the over-fitting, two constraints are further introduced to merge more details from the visible image and prevent the luminance degradation for the fused result, respectively. Moreover, joint norms are exploited to produce a better result.${{\boldsymbol{l}}_{2,1,{\boldsymbol{rc}}}}$provides the structural group sparseness for the fidelity term, whereas${{\boldsymbol{l}}_{1/2}}$presents the better gradient sparse for the detail preserving term and${{\boldsymbol{l}}_2}$is utilized for the luminance degradation preventing term. Experimental results indicate that the proposed method can give state-of-the-art performances both in visual perception and quantitative scores than other methods.

This work investigates a reduced-complexity adaptive methodology to consensus tracking for a team of uncertain high-order nonlinear systems with switched (possibly asynchronous) dynamics. It is well known that high-order nonlinear systems are intrinsically challenging as feedback linearization and backstepping methods successfully developed for low-order systems fail to work. Even the adding-one-power-integrator methodology, well explored for the single-agent high-order case, presents some complexity issues and is unsuited for distributed control. At the core of the proposed distributed methodology is a newly proposed definition for separable functions: this definition allows the formulation of a separation-based lemma to handle the high-order terms with reduced complexity in the control design. Complexity is reduced in a twofold sense: the control gain of each virtual control law does not have to be incorporated in the next virtual control law iteratively, thus leading to a simpler expression of the control laws; the power of the virtual and actual control laws increases only proportionally (rather than exponentially) with the order of the systems, dramatically reducing high-gain issues.

This article investigates the synchronization of stochastic delayed neural networks under pinning impulsive control, where a small fraction of nodes are selected as the pinned nodes at each impulsive moment. By proposing a uniformly stable function as a new tool, some novel mean square decay results are presented to analyze the error system obtained from the leader and the considered neural networks. For the divergent error system without impulsive effects, the impulsive gains of pinning impulsive controller can admit destabilizing impulse and the number of destabilizing impulse may be infinite. However, if the error system without impulsive effects is convergent, to achieve the synchronization of the stochastic neural networks, the growth exponent of the product of impulsive gains can not exceed some positive constant. It is shown that the obtained results increase the flexibility of the impulsive gains compared with the existing results. Finally, a numerical example is given to illustrate the practicality of synchronization criteria.

In this article, the finite-time synchronization (FTSYN) of a class of quaternion-valued neural networks (QVNNs) with discrete and distributed time delays is studied. Furthermore, the FTSYN and fixed-time synchronization (FIXSYN) of the QVNNs without time delay are investigated. Different from the existing results, which used decomposition techniques, by introducing an improved one-norm, we use a direct analytical method to study the synchronization problems. Incidentally, several properties of one-norm of the quaternion are analyzed, and then, three effective controllers are proposed to synchronize the drive and response QVNNs within a finite time or fixed time. Moreover, efficient criteria are proposed to guarantee that the synchronization of QVNNs with or without mixed time delays can be realized within a finite and fixed time interval, respectively. In addition, the settling times are reckoned. Compared with the existing work, our advantages are mainly reflected in the simpler Lyapunov analytical process and more general activation function. Finally, the validity and practicability of the conclusions are illustrated via four numerical examples.