This paper investigates the secure optimal load frequency control (LFC) problem for Markovian multi-area power systems under false data injection (FDI) attacks. First, by employing the Markov jump process to describe the power system’s state changes induced by FDI attacks, a Markovian multi-area power system model is constructed. Subsequently, considering the secure optimal LFC design of the Markovian multi-area power system, coupled Riccati equations (CREs) are derived. Then, to solve these CREs, an adaptive weight-dependent iteration algorithm is proposed. By progressively increasing the weight of the latest step information, the algorithm can accelerate convergence. Finally, the effectiveness of the secure LFC is validated through a simulation example of a Markovian three-area power system. Compared with the fixed weight iteration algorithm, the proposed adaptive weight-dependent iteration algorithm exhibits faster convergence and lower residuals.

Euler-Lagrange multi-agent systems (MASs), which are typical IoT systems, have wide applications including autonomous underwater vehicles, mobile robots and robot manipulators. As a crucial aspect of Euler-Lagrange MASs, the consensus becomes a research priority. However, most of the existing works on consensus of Euler-Lagrange MASs are based on the assumption of linearized parameters, and the designed distributed event-triggered (ET) control protocols are only applicable to the leader-following consensus (LFC) of uncertain Euler-Lagrange MASs with undirected graphs. Hence, the Leaderless consensus (LLC) of uncertain Euler-Lagrange MASs with digraphs is studied in this paper. Based on fuzzy logic system (FLS), a fully distributed ET adaptive control protocol is designed, and new theoretical analysis results are proposed in Lemmas 3–5. By the proposed fully distributed ET fuzzy control protocol, the communication resources are effectively saved, and the LLC for uncertain Euler-Lagrange MASs without the assumption of linearized parameters is achieved. In the end, based on networked two-linked robot manipulators, simulations are given to show the validity of the proposed results. In comparison with the method of [28], our method with σi= 0.005 provides 263.48%, 245.87%, 237.15%, 210.46% and 210.72% less information exchange on manipulators 1-5, respectively.

The large character set CAPTCHA is an important extension of the traditional text-based CAPTCHA with larger alphabet languages to defend against automated attack programs. However, the state-of-the-art deep learning attacks have cracked such CAPTCHA. Existing defenses against such threats increase the complexity of CAPTCHA, thus decreasing usability. We propose ACG (Adversarial Large Character Set CAPTCHA Generation), a framework with two modules: aFine-grained Generation Module, combining three novel strategies to prevent attackers from recognizing characters, and anEnsemble Generation Moduleto generate global perturbations in CAPTCHAs. It not only strengthens defense against recognition attacks but also improves robustness against diverse detection architectures through adversarial perturbations. Additionally, we develop a toolkit, Adv-Eval, consisting of CAPTCHA datasets from 10 of the most popular Chinese CAPTCHA schemes and benchmarking various attacks. We conduct extensive experiments using Adv-Eval to demonstrate ACG's efficacy, especially manifesting a significant decrease in the average success rate of diverse attacks from 51.52% to 2.56%. To the best of our knowledge, ACG is the first framework to defend large character set CAPTCHAs against detection attacks using transferable adversarial examples.

This paper considers intelligent event-triggered${H}_{\infty }$Load Frequency Control (LFC) for a new kind of multi-area power system. Firstly, delay factors are introduced into the prime mover, engine, and energy storage unit, i.e, multiple-resource delays, and the impact of these delays on the dynamic behavior of system is discussed for the first time, making the model more realistic and accurate. Secondly, a new lemma is proposed, which introduces free variables, extending to the scenarios with time delays. Combining this lemma with the event-triggered mechanism, the control scheme is designed, which is featured by two characteristics. On the one hand, in the Lyapunov functional, looped terms on neighbor triggering instants are considered, reduce the conservatism of the obtained criteria by capturing the information of triggered instants. Meanwhile, a mandatory triggering mechanism is introduced to break the possible dead loop caused by long-term no-triggering. On the other hand, a genetic algorithm (GA) is employed to optimize the parameters of the trigger threshold, encouraging efficient control within the context of LFC and delays. The effectiveness of the proposed approach is confirmed by a typical numerical example with four case studies.

This article is focused on the secure consensus of multi-agent systems (MASs) with Euler-Lagrange (EL) dynamics and multiple DoS attacks. First, a new model of the multiple DoS attacks is built, which is based on discrete sampled-data communication and considers the joint impact of the multiple DoS attacks. Second, under multiple DoS attacks, two new technical results are proposed, which lay a good foundation for consensus analysis. Then, by designing an adaptive distributed control (DC) protocol combining with two new auxiliary systems, secure consensus results are derived for MASs with EL dynamics and multiple DoS attacks. Finally, simulations based on networked two-linked robot manipulators are presented to show the effectiveness of the theoretical results.

This article investigates the secure consensus problem for nonlinear leader-following multiagent systems (MASs) under denial-of-service (DoS) attacks. First, an improved memory-based adaptive event-triggered mechanism (MAETM) is proposed to reduce data redundancy and save network resources. Unlike previous MAETMs, in order to effectively prevent excessively long data triggering periods or overly frequent triggering, the proposed MAETM introduces an upper limit and a lower limit to limit the threshold range. In this way, the communication resources can be effectively saved. In addition, considering the impact of DoS attacks, a new dynamic-memory-weight-dependent (DMW-dependent) security control protocol is proposed. Unlike control methods that use fixed weights, the protocol dynamically adjusts the weights of historically released packets according to DoS attacks, thus more fully utilizing the information of successfully transmitted packets to mitigate the impact of DoS attacks. Subsequently, sufficient conditions for the secure consensus of MASs are derived by constructing Lyapunov–Krasovskii functionals (LKFs) and using the law of large numbers and the Lagrange mean value theorem. Finally, two numerical simulations are provided to verify the effectiveness of the proposed MAETM and DMW-dependent security control protocol.

In this article, the resilient secure synchronization of complex networks (CNs) that are subject to denial-of-service (DoS) attacks is studied. In contrast to existing logic processors, a new processor in which more essential information on DoS attacks, such as the number of sampling instants being attacked and attack moment being detected, can be captured is designed. Based on the benefits of the logic processor, a switching sampled-data (SD) control protocol in which different feedback gains are chosen for different attack cases is proposed. In contrast to existing control schemes, the switching SD control protocol is more flexible. Subsequently, according to different attack cases, a switching Lyapunov-Krasovskii function (LKF) that can effectively fulfil the switching SD control protocol is founded. New resilient secure synchronization criteria that can successfully counteract the effects of DoS attacks are then established for CNs based on the switching SD control protocol and switching LKF. Finally, a complex Chua’s circuit system is used to give evidence of the feasibility and superiority of the proposed method.

Under the framework of multiagent systems (MASs), this article is focused on the leaderless consensus of multiple Euler–Lagrange (EL) systems subject to denial-of-service (DoS) attacks. First, a new joint control protocol, which combines the distributed sampled-data (SD) control and adaptive distributed control (DC), is designed. The distributed SD control is equipped with logic processors, which can capture key information of DoS attacks, and the adaptive DC is conducive to relaxing some generally required constraint conditions. Second, a new property of a non-negative differentiable function is proposed, which is very helpful in solving the distributed SD control issue of multiple EL systems. Third, by setting up a$\textbf{W}$-dependent Lyapunov–Krasovskii functional (LKF) and utilizing the property of the non-negative differentiable function, new leaderless consensus results are derived for multiple EL systems with DoS attacks. The obtained leaderless consensus results are in the shape of linear matrix inequalities (LMIs) and can efficiently counter the influence of the DoS attacks. Ultimately, the effectiveness of the derived results is inspected by an MAS with multiple two-linked robot manipulators.

This article focuses on designing an event-triggered impulsive fault-tolerant control strategy for the stabilization of memristor-based reaction-diffusion neural networks (RDNNs) with actuator faults. Different from the existing memristor-based RDNNs with fault-free environments, actuator faults are considered here. A hybrid event-triggered and impulsive (HETI) control scheme, which combines the advantages of event-triggered control and impulsive control, is newly proposed. The hybrid control scheme can effectively accommodate the actuator faults, save the limited communication resources, and achieve the desired system performance. Unlike the existing Lyapunov-Krasovskii functionals (LKFs) constructed on sampling intervals or required to be continuous, the introduced LKF here is directly constructed on event-triggered intervals and can be discontinuous. Based on the LKF and the HETI control scheme, new stabilization criteria are derived for memristor-based RDNNs. Finally, numerical simulations are presented to verify the effectiveness of the obtained results and the merits of the HETI control method.

This article focuses on the design of a mode- dependent adaptive event-triggered control (AETC) scheme for the stabilization of Markovian memristor-based reaction-diffusion neural networks (RDNNs). Different from the existing works with completely known transition probabilities, partly unknown transition probabilities (PUTPs) are considered here. The switching conditions and values of memristive connection weights are all correlated with Markovian jumping. A mode-dependent AETC scheme is newly proposed, in which different adaptive event-triggered mechanisms will be applied for different Markovian jumping modes and memristor switching modes. For each given mode, the corresponding event-triggered mechanism can efficiently reduce the number of transmission signals by adaptively adjusting the threshold. Thus, the mode-dependent AETC scheme can effectively save the limited network communication resources for the considered system. Based on the proposed control scheme, a new stabilization criterion is set up for Markovian memristor-based RDNNs with PUTPs. Meanwhile, a memristor-dependent AETC scheme is devised for memristor-based RDNNs. Finally, simulation results are presented to verify the effectiveness and superiority of the analysis results.

The synchronization in probability of reaction-diffusion neural networks (RDNNs) with stochastic sampling is studied in this article. By introducing a stochastic switching parameter, a new switching system protocol is proposed for stochastic sampling control systems. The switching system protocol effectively improves the existing methods. By the protocol, the stochastic switching sampled-data controller is designed, and the considered system is transformed into a switching system. Different from the existing sampled-data controllers with determined control gains, the stochastic switching sampled-data controller is with switching gains, which is more elastic. Then, by constructing a new stochastic switching Lyapunov–Krasovskii functional (LKF), using the law of large numbers and the Lagrange mean value theorem, new synchronization in probability criteria are established for RDNNs. In the mean time, the wanted stochastic switching sampled-data controller gains are obtained. Moreover, the synchronization in probability issue is also studied for NNs with stochastic sampling. Finally, the effectiveness of the proposed results are verified by two numerical examples.

The asymptotical synchronization in mean square of reaction–diffusion neural networks (RDNNs) with random delays is studied in this article. By sampling on both the time domain and spatial domain, a time–space sampled-data controller (TSSDC) is designed, which can efficiently save the network communication resources for RDNNs. A new processing method for the TSSDC is provided. Compared with the existing methods, the processing method here can capture more sampling information and is more concise. An extended Poincaré–Wirtinger inequality is proposed, which is in matrix form and less conservative. Then by constructing a sampling-dependent LKF, using the extended Poincaré–Wirtinger inequality and Hölder inequality, new mean square asymptotical synchronization criteria are set up for RDNNs with random delays, and the desired TSSDC gain is obtained. At length, a numerical example is given to verify the effectiveness and superiority of the obtained results.

Recently, researchers have been dedicated to discovering Generative Adversarial Network (GAN) artifacts and using them to identify generated images. However, current approaches exhibit restricted performance when testing against unseen GAN models, which is also known as the cross-domain scenario. To overcome this limitation, we propose a novel GAN-generated image detection framework by estimating artifact similarity, which is inspired by relation network. The proposed method consists of two stages, including representation learning and representation comparison. For representation learning, ResNet-50 equipped with Instance Normalization in the Shallow layers (ResNet-INS) is constructed as the embedding network to extract generalized features. For representation comparison, Category and Domain-Aware loss function (CDA loss) is designed by leveraging both category and domain information efficiently, which can enlarge inter-class discrepancy of different categories (GAN-generated or pristine images) and improve intra-class compactness from different domains (source attributions) in the same category. Extensive experiments are conducted which consider various cross-domain scenarios to verify the generalization of the proposed method. Besides, our method exhibits satisfying robustness against common post-processings, even when data augmentation is not considered during the training stage.

In this article, a general approach to fixed-time synchronization problem is investigated for the general system of fractional-order multidimension-valued fuzzy memristive neural networks. First, we complete the establishment of the new model which is so general that we can regard it as fractional-order real-valued fuzzy memristive neural networks, fractional-order complex-valued fuzzy memristive neural networks, and fractional-order quaternion-valued fuzzy memristive neural networks. Then, we mainly apply two new general inequalities such as extended Cauchy–Schwarz inequality and generalized derivative of fractional-order absolute value function in order to realize the general analysis on the discussed problem. Owing to the two new lemmas, we can construct the general Lyapunov–Krasovskii functional with adjustable coefficients, design the nonlinear controllers with fuzzy gains, as well as acquire the flexible criteria with several useful factors. Particularly, the acquisition of the less conservative fixed time benefits from the new controllers which not only contains the common feedback gains but also can be comprised of the general coefficients and the fuzzy gains. Finally, a numerical example is provided to demonstrate our theoretical results.

This article focuses on the design of fuzzy secure control for a class of coupled systems, which are modeled by a nonlinear$N$-dimensional ($N$-D) parabolic partial differential equation (PDE) subsystem and an ordinary differential equation (ODE) subsystem. Under stochastic deception attacks, a fuzzy secure control scheme is designed, which is effective to tolerate the attacks and ensure the desired performance for the considered systems. A new fuzzy-dependent Poincare–Wirtinger’s inequality (PWI) is proposed. Compared with the traditional Poincare’s inequality, the fuzzy-dependent PWI is more flexible and less conservative. Meanwhile, an augmented Lyapunov–Krasovskii functional (LKF) is newly constructed, which strengthens the correlations of the PDE subsystem and ODE subsystem. Then, on the ground of the fuzzy-dependent PWI and the augmented LKF, new exponential stabilization criteria are set up for the PDE-ODE coupled systems. Finally, a hypersonic rocket car is presented to verify the effectiveness and less conservatism of the obtained results.

This study focuses on the quasisynchronization problem for reaction–diffusion neural networks (RDNNs) in the presence of deception attacks. Under deception attacks, a time–space sampled-data (TSSD) control mechanism is proposed for RDNNs. Compared with traditional control strategies, the proposed control mechanism can not only save network bandwidth but also improve the cybersecurity of communications. Inspired by Halanay’s inequality, a new inequality is proposed, which can be effectively applied to the quasisynchronization problem for dynamical systems. Then, by using this inequality and the Lyapunov functional approach, quasisynchronization criteria are set for RDNNs. The desired control gain is gained from solving a group of linear matrix inequalities. Moreover, in the absence of deception attacks, the exponential synchronization problem is studied for RDNNs. In the end, simulation results are given to demonstrate the usefulness of the theoretical analysis.

Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properties when original videos are recompressed by the increased and decreased bit rates. It motivates us to propose a two-stage cascaded detection scheme for double HEVC compression based on temporal inconsistency to overcome limitations of existing methods. For a given video, CU information maps are extracted from each short-time video clip using our proposed value mapping strategy. In the first detection stage, a compact feature is extracted based on the distribution of different CU types and Kullback–Leibler divergence between temporally adjacent frames. This detection feature is fed into the Support Vector Machine classifier to identify abnormal frames with the increased bit rate. In the second stage, a shallow convolutional neural network equipped with dense connections is designed carefully to learn robust spatiotemporal representations, which can identify abnormal frames with the decreased bit rate whose forensic traces are less detectable. In experiments, the proposed method can achieve more promising detection accuracy compared with several state-of-the-art methods under various coding parameter settings, especially when the original video is recompressed with a low quality (e.g., more than 8%).

This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

This article focuses on the design of a fuzzy adaptive event-triggered sampled-data control (AETSDC) scheme for stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks (MNNs) with reaction-diffusion terms (RDTs). Different from the existing T-S fuzzy MNNs, the reaction and diffusion phenomena are considered, which make the presented model more applicable. A fuzzy AETSDC scheme is proposed for the first time, in which different AETSDC mechanisms will be applied for different fuzzy rules. For each fuzzy rule, the corresponding AETSDC mechanism can be promptly adaptively adjusted based on the current and last sampled signals. So the fuzzy AETSDC scheme can effectively save the limited communication resources for the considered system. By introducing a suitable Lyapunov- Krasovskii functional, new stability and stabilization criteria are established for T-S fuzzy MNNs with RDTs. Meanwhile, the desired fuzzy AETSDC gains are obtained. Finally, simulation results are given to verify the superiority of the fuzzy AETSDC scheme and the effectiveness of the theoretical results.

Detection of double compression is regarded as one primary step in analyzing the integrity of digital videos, which is of prominent importance in video forensics. However, current methods are vulnerable with the severe lossy quantization in the recompression process such that it is challenging to obtain reliable frame-wise detection results, especially for the high efficiency video coding (HEVC) standard. In view of these issues, in this paper, a hybrid neural network is proposed to reveal abnormal frames in HEVC videos with double compression by learning robust spatio-temporal representations from coding information in the compression domain. Based on the statistical analysis of Coding Units (CUs), it is interesting to find that HEVC video streams contain “rich” coding information that could be leveraged to identify abnormal traces caused by double compression. Two types of coding information maps, including CU Size Map (CSM) and CU Prediction mode Map (CPM), are exploited. In contrast with the conventional paradigm relying on pixel-level representations of decoded frames, CSMs and CPMs of a short-time video clip are treated as the input, aiming to achieve high robustness against recompression of low quality. In our hybrid neural network, an attention-based two-stream residual network is proposed to learn hierarchical representations from CSM and CPM, which are then jointly optimized by the attention-based fusion module. Finally, the temporal variation is modeled by Long Short-Term Memory (LSTM) to obtain frame-wise detection results. We have conducted extensive experiments considering various video content and coding parameters, such as bitrates and sizes of Group of Picture. Experimental results show that our approach can obtain state-of-the-art performance compared with conventional methods, especially when videos are recompressed in the low bitrate coding scenarios.

This article is concerned with the problem of the global Mittag-Leffler synchronization and stability for fractional-order quaternion-valued neural networks (FOQVNNs). The systems of FOQVNNs, which contain either general activation functions or linear threshold ones, are successfully established. Meanwhile, two distinct methods, such as separation and nonseparation, have been employed to solve the transformation of the studied systems of FOQVNNs, which dissatisfy the commutativity of quaternion multiplication. Moreover, two novel inequalities are deduced based on the general parameters. Compared with the existing inequalities, the new inequalities have their unique superiorities because they can make full use of the additional parameters. Due to the Lyapunov theory, two novel Lyapunov-Krasovskii functionals (LKFs) can be easily constructed. The novelty of LKFs comes from a wider range of parameters, which can be involved in the construction of LKFs. Furthermore, mainly based on the new inequalities and LKFs, more multiple and more flexible criteria are efficiently obtained for the discussed problem. Finally, four numerical examples are given to demonstrate the related effectiveness and availability of the derived criteria.

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.

This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.

This paper focuses on the synchronization problem of Takagi-Sugeno (T-S) fuzzy complex networks with partial and discrete-time couplings via event-triggered sampling control. Different from traditional control methods, a more general and practical event-triggered communication scheme with nonuniform sampling is newly designed for T-S fuzzy complex networks. Then, a Lyapunov-Krasovskii functional (LKF) with a novel input-delay-product-type (IDPT) term is presented. The IDPT term can fully capture the information of the nonlinear functions and the actual sampling pattern. Based on the new IDPT LKF, less conservative synchronization criteria are derived. Meanwhile, by solving a set of linear matrix inequalities, the desired pinning control gains are precisely obtained. Simulation examples are provided to illustrate the effectiveness and superiorities of the proposed results.

The stability of delayed Markovian generalized neural networks is studied where the transition rates of the modes are partly unknown. The partly unknown transition rates generalize the traditional works that are with all known transition rates. Then, a Lyapunov-Krasovskii functional (LKF) with a delay-product-type (DPT) term is constructed. The DPT term is not only simple but also fully utilizes the information of time delay. Based on the new DPT LKF, stability criteria are presented, which are with lower computational complexity and less conservative. In the end, the validity and superiorities of the analytical results are verified by several examples.

This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results. Third, based on the TDDM approach and applying Wirtinger's inequality and improved reciprocally convex inequality, stability criteria are derived. In comparison with some existing results, our results are not only less conservative but also involve lower calculation complexity. Finally, numerical examples are provided to show the effectiveness and advantages of the proposed results.

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.

In this paper, the problem of nonfragile sampled-data synchronization of delayed complex dynamical networks with randomly occurring controller gain fluctuations (ROCGFs) is studied. First, more applicable nonfragile memory sampled-data controllers are designed, which involve the signal transmission delay and ROCGFs. The controller gain fluctuations appear in a random way, which obey certain Bernoulli distributed white noise sequences. Second, a modified piecewise Lyapunov-Krasovskii functional (LKF), which involves cubic sawtooth structure term, is constructed for the first time. Third, based on the LKF, less conservative synchronization criteria are established. In comparison with the existing results, the constraint condition of the positive definition of the LKF is less restrictive, since it does not need to be positive definite for all time, but is only required to be positive definite at sampling times. Finally, the effectiveness and advantages of the obtained results are illustrated by two numerical examples.