This paper develops a novel dynamic sampled-data-based event-triggered (DSET) secure fuzzy tracking control scheme for a category of unknown nonlinear heterogeneous multiagent systems (MASs). Asynchronous DSET mechanisms are developed, in which the triggering thresholds evolve dynamically with the system states. The proposed mechanisms only require discrete state sampling and triggering evaluation, thereby avoiding continuous monitoring and reducing triggering frequency. Moreover, the triggering interval is shown to admit a positive lower bound that is not solely determined by the sampling period. Under denial-of-service (DoS) attacks, distributed DSET observers are constructed to estimate the leader's system matrices and states, where only a subset of followers requires access to the leader information. A detection scheme is further designed to identify the termination of DoS attacks. Furthermore, a fuzzy fault-tolerant control scheme is proposed based solely on output measurements. The convergence of observations and tracking errors is rigorously established. Simulation results demonstrate the effectiveness and superiority of the proposed approach.

This article proposes a novel distributed neuro-adaptive 3-D formation tracking control framework of multiple autonomous underwater vehicles (multi-AUVs) subject to marine environmental disturbances. On the one hand, we assume that all AUVs can obtain the real-time states. By introducing a series of variable transformations, the multi-AUV system is transformed into an underactuated nonlinear system with virtual control input. Radial basis function neural networks (RBFNNs), whose weights are updated online, are utilized to approximate nonlinear functions. Considering environmental disturbances, a virtual controller is designed such that all AUVs track the leader while maintaining the desired formation geometry. Then, the actual controller is given as an adaptive form according to the virtual control signals. On the other hand, we assume that all AUVs can only obtain the sampling states of themselves and their neighbors under the predefined event-triggered conditions. Multi-AUV system is transformed into a second-order system with complex nonlinear dynamics, then their states are reconstructed via a neuro-adaptive state observer using sampling states, and a virtual controller is proposed such that all AUVs track the leader while maintaining the desired formation geometry under local communication with no Zeno behavior. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed control design.

In this paper, the fixed-time intra/inter-layer output synchronization problem of output-coupled multiplex networks is investigated utilizing a dynamic event-triggered control method. Firstly, to solve the issue of unavailability of node states resulting from uncontrollable factors, a multiplex networks model with observable intra/inter-layer output coupling information is constructed. Subsequently, two dynamic event-triggered control strategies based on output information are proposed, on the basis of which the fixed-time output synchronization criteria are established and Zeno behavior is excluded. The controllers proposed in this paper replace the common linear terms and multiple power-law terms in the existing fixed-time controllers with an exponential term based on the output errors, and also no longer include the intra/inter-layer coupling information of the nodes, making the form of the controllers more streamlined and easier to implement the control strategies. Finally, the effectiveness of the designed control protocols is verified by some numerical simulations based on Chua’s circuit as well as spacecraft formation control.

In this article, the event-triggered finite-time stabilization of time-scale delayed Takagi-Sugeno (T-S) fuzzy systems is studied. By comparing strategies, inequality techniques, and time scale theory, finite-time stabilization criteria for the systems are derived that do not require differentiability of the time delay, and the controller is designed in a simple form that does not rely on power functions or delayed state feedback controllers. Corresponding results cover both continuous-time and discrete-time cases, and construct a unified theoretical framework for the finite-time analysis of the time-scale delayed systems. Meanwhile, the proposed event-triggered mechanism can avoid Zeno behavior and reduce the consumption of communication resources. The validity of the theoretical results is verified by two simulation experiments.

In this article, leader-following consensus of time-scale-type heterogeneous nonlinear multiagent systems (HNMASs) is investigated with dynamic periodic event-triggered mechanism (DPETM). The event detection period in DPETM is determined by a function-dependent threshold, whose initial value and the value at each periodic event detection instant are used for the update of an auxiliary function in the DPETM. Furthermore, the auxiliary function with periodic jumps serves as a detection threshold. To guarantee the nonincreasing behavior of the designed non-negative analysis function, a weighted function is devised that shares the same derivative form as the function that determines the detection period during each detection period. Then, by integrating the theory of time scales and graph theory, leader-following consensus is achieved in a periodic communication fashion with fewer sampling updates. Two examples are presented to illustrate the validity of the results.

This article addresses the problem of fixed-time bipartite synchronization (FxTBS) of signed networks (SNs) affected by impulses. First, this article constructs a model of SNs that captures the multilayer properties of the network and takes into account the influence of nonlinear coupling strengths between nodes. To overcome the challenges brought by the introduction of nonlinear coupling strengths, this article adopts a Takagi–Sugeno fuzzy model to characterize the nonlinear variation of coupling strengths reasonably. Then, in the framework of average impulsive interval applicable to a wider range of impulsive signals, this article proposes a novel method for analyzing the fixed-time stability of impulsive systems, which not only loosens the restriction of the derivative of the Lyapunov function in the existing studies, but also gives a more accurate estimation of the settling time, and more importantly, provides a theoretical basis for designing appropriate impulsive signals to modulate the dynamic behavior of SNs toward achieving the desired goal. Based on the newly suggested method, this article derives a unified synchronization criterion suitable for evaluating the implementation of FxTBS of SNs under both desynchronizing and synchronizing impulses. Finally, this article visualizes the correctness of the aforementioned theoretical results utilizing a widely used numerical example.

This article develops a methodology employing partial differential equations (PDEs) to facilitate the exponential deployment of large-scale heterogeneous nonlinear multiagent systems (MASs). The considered MASs comprise a multitude of nonlinear first-order agents (FOAs) and second-order agents (SOAs). Two heterogeneous nonlinear PDEs are established to model the considered MASs by designing appropriate network communication protocols. Unlike previous PDE-based approaches for multiagent deployment, the topological weights between neighboring agents are defined as series-dependent. An informed agent, which is able to measure the location information of other agents and transmit its location information to neighboring agents through the communication network, is placed between the final FOA and the initial SOA. This novel network-based control scheme is referred to as single-point control, which could ensure the well-posedness and exponential stability of the error system. Accordingly, pointwise and distributed measurements are employed for delay-free and time-delayed cases, respectively. Numerical examples are provided in 3-D space to substantiate the obtained theoretical results.

In this article, the bipartite consensus problem of multiagent systems on time scales under switching topologies is investigated. A dynamic event-triggered control strategy is designed to reduce the number of triggers. Sufficient condition to guarantee the consensus is obtained by constructing a non-negative function and combining with the theory of time scale calculus. In addition, it is proved through a categorical discussion that the entire triggering sequence determined by the switching topology and the triggering function does not display Zeno behavior. Lastly, to confirm that the theoretical results are feasible, a numerical simulation and an application to spacecraft formation flight are provided.

This article investigates the practical finite-time spatial deployment of a class of large-scale heterogeneous nonlinear multi-agent systems (MASs), for which a novel hybrid analysis methodology based on ordinary differential equations (ODEs) coupled with partial differential equations (PDEs) is proposed. The assumption is made that a portion of the agents is sparsely distributed in space, while the other portion is densely distributed. By designing appropriate network communication protocols (NCPs), the dynamics of MASs are represented by a hybrid model consisting of several ODEs and a PDE. Particularly, the network topological weights are specifically designed as semi-Markov switched to better align with real communication situations of MASs, while complying with inconsistent switching rules. Moreover, for delay-free and time-delayed cases, this article proposes two novel projection-based adaptive neural control schemes and obtains two design criteria of controller gains, such that the practical finite-time stability of the tracking error systems could be guaranteed. Finally, numerical examples are provided to illustrate the effectiveness of the developed approaches.

This article establishes a series of theoretical findings on the sampled-data-based event-triggered (SET) output-feedback consensus for a class of heterogeneous high-order multiagent systems (MASs) with mismatched parametric uncertainties. First, a distributed SET leader observer is devised for each agent to estimate the information of leader, and its convergence is rigorously proven based on matrix theory and analysis approaches. Second, a continuous state observer is developed using solely SET output signal influenced by sensor faults is proposed to avoid the nondifferentiability of the virtual controller, and the SET output-feedback control protocol is designed via the backstepping technique. It is proven that the semi-global output consensus problem can be addressed through the proposed controller. Different from the existing work, the consensus protocol presented in this article further saves communicational and computational resources, because the SET mechanisms are deployed to each channel of MASs which merely need to discretely monitor the event-triggered (ET) conditions at sampling instants and transmit the information at triggered time. Besides, the Zeno behavior can be trivially prevented. Finally, a simulation example is depicted to demonstrate the validity of the proposed theoretical results.

Unmanned Aerial Vehicles (UAVs) equipped with multiple tasking capabilities will play an increasingly important role in future warfare scenarios. As a battlefield environment may have various of tasks and corresponding constraints, the strategic selection of tasks is important to improve the performance of the group of UAVs. This paper addresses this problem through the Environment Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. Considering constraints of task requirements and resource limitations, we propose a hybrid approach that integrates genetic algorithms and linear programming to generate an optimal set of task choices. Our experimental results highlight the effectiveness of linear programming in efficiently achieving optimal solutions within small teams. At the same time, the combination of genetic algorithms and linear programming proves effective in ensuring satisfactory solutions within an acceptable time for larger teams. This research paves the way for optimizing the task selection in complex operational environments.

For large-scale heterogeneous nonlinear multi-agent systems (MASs) consisting of abundant first-order and second-order agents, this paper presents a novel framework based on partial differential equations (PDEs) to facilitate their practically finite-time deployment in 2D or 3D space. First, through designing appropriate network communication protocols (NCPs), a heterogeneous nonlinear PDE model composed of a heat equation and a damped wave equation is constructed to characterize the collective dynamics of considered heterogeneous nonlinear MASs. Second, a single-point control strategy and a double-boundary control strategy are proposed, which could not only ensure the well-posedness of the closed-loop heterogeneous PDEs but also enable the finite-time deployment of multi agents. Notably, to better align with real MASs and operating environment, the network topologies and controllers are designed to be semi-Markov switched, while adhering to multiple asynchronous switching rules. Third, with the designed NCPs and control schemes, several sufficient conditions are derived to guarantee the practically finite-time stability of error systems. Finally, two numerical examples and an application example are conducted to validate effectiveness and practicability of the developed approaches.

Based on role-based collaboration (RBC), group role assignment (GRA) optimizes a team’s overall performance by assigning the most appropriate individual agents from the team’s viewpoint based on agents’ role-playing abilities. As an extension of GRA, GRA with a training plan (GRATP) deals with the impact of training on team management. Considering the correlation between roles, the training of one agent on one role also affects the performance of the agent in other roles. Moreover, in the adaptive collaboration (AC) problem, the training time also affects significantly the agent’s ability, as an agent’s ability changes over time. However, the existing GRATP models fail to consider these factors in the collaboration process. Therefore, we aim to address the role-correlation-based adaptive GRATP (RCA-GRATP) in this article. This article contributes two aspects to the literature on AC. 1) RCA-GRATP problem is abstracted based on RBC and GRA. To the best of the authors’ knowledge, this is the first article that explicitly considers role correlation in the RBC problems. 2) A comprehensive formalization of RCA-GRATP and two solving algorithms for diverse situations are proposed to solve the formalized problems. Experiments are carried out to verify the effectiveness of the proposed algorithms in diverse scenarios.

In this article, global exponential stabilization of Takagi–Sugeno (T–S) fuzzy systems with discrete time-varying delays on time scales under denial-of-service (DoS) attacks is investigated. When a DoS attack occurs, the control channel is blocked and the controller is disabled. Combining analytical method, inequality techniques, and time scale theory, stabilization criterion for the underlying systems is obtained via a fuzzy controller. Furthermore, the corresponding outcomes on continuous and discrete time domains are provided, respectively. Finally, two numerical simulations and an application of the Chua's circuit are exhibited to validate the effectiveness of the theories.

In this article, exponential synchronization of complex dynamical networks (CDNs) on time scales is researched. An IDET control strategy is designed to decrease the number of the event-triggered updating instants. Leveraging intermittent event detections and event-triggered sampling, and combining the analytical method with the time-scale theory, synchronization criteria are obtained for the underlying CDNs. Moreover, a parameter selection algorithm is given to acquire control parameters. In addition, two lemmas on exponential functions of time scales are proposed to prove the exclusion of Zeno behavior. Two numerical simulations and an application of formation control of spacecrafts are given to verify the validity of theoretical results.

The reinforcement and extinction in conditioned reflex have been studied extensively, but memristor-based generalization and differentiation circuits under different emotional conditions are rarely studied. Therefore, a memristor-based generalization and differentiation circuit under positive and negative emotional conditions is presented in this paper. The circuit includes emotion module, synapse module, voltage selection module and output module. The emotion module is divided into positive emotion and negative emotion modules. Different emotions have different effects on the synapse module, which in turn affects the output module. The memristor-based circuit proposed in this paper can not only realize the process of generalization and differentiation under the influence of different emotions, but also realize the function of secondary differentiation. The results presented in this paper can be verified in PSPICE. By analyzing the effects of different emotions on differentiation and generalization, this paper provides some references for future researches in the field of generalization and differentiation.

This article focuses on the finite-time stabilization problem for fuzzy spatiotemporal competitive neural networks (FSCNNs) with discrete and distributed delays. First, the differentiable conditions for discrete and finite distributed delays in FSCNNs are removed, and the constraints of the kernel function in infinite distributed delays are weakened. Then, a novel partial differential inequality is proposed to handle the spatial diffusions, which relaxes the restriction for symmetric around the origin of the bounded spatial domain. To stabilize FSCNNs within a finite time, a novel control strategy without delay-dependent terms is established. Moreover, different from the existing works, a more succinct Lyapunov functional is constructed, which does not need to include multiple integral type functional terms to eliminate the influence of the hybrid delays. By virtue of the comparison method and inequality techniques, several sufficient criteria are deduced to guarantee the finite-time stabilization of FSCNNs. Finally, simulations are presented to illustrate the feasibility and effectiveness of the theoretical results.

Seldom existing studies directly focus on the control issues of 2-D spatial partial differential equation (PDE) systems, although they have strong application backgrounds in production and life. Therefore, this article investigates the finite-time control problem of a 2-D spatial nonlinear parabolic PDE system via a Takagi–Sugeno (T–S) fuzzy boundary control scheme. First, the overall fuzzy system model is constructed using T–S fuzzy rules to approximate the original nonlinear system. Second, based on the planar distributed measurement, boundary collocated measurement, and linear measurement methods, three novel kinds of fuzzy boundary controllers are designed, respectively. Then, by employing the variable substitution and integral inequality techniques, three criteria that ensure the finite-time boundness of the considered system are obtained. Finally, simulations of main results are provided to verify the effectiveness and practicability of the proposed measurement and control schemes.

This article is concerned with global exponential synchronization of delayed fuzzy neural networks with reaction diffusions (RDFNNs). By adopting analytic method and some inequality techniques, a global exponential synchronization criterion in terms of$p$-norm ($p\geq 2$) is obtained for the RDFNNs via adaptive intermittent control. One numerical example is provided to demonstrate the validity of the proposed outcomes. Two other examples are given to show the applications in image encryption and pseudorandom number generation, respectively.

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

Training is an effective way to improve agents’ performance. As an extension of group role assignment (GRA), GRA with a training plan (GRATP) aims to maximize the group performance or benefit by finding the optimal role assignment and training plan. However, GRATP has only been discussed in static scenarios. In dynamic environments, agents’ performance changes over time and adaptive collaboration is designed to keep the group in a good state. Therefore, this paper investigates the GRATP problem in adaptive collaboration. The timing of training has a significant impact on the improvement of individual performance, which will in turn affect the team performance and total benefit. By utilizing Role-Based Collaboration and GRA, the optimal training timing and training plan are obtained, which maximize the total benefit of the group. After training, the roles are re-assigned to the agents based on their current performance. This paper’s contributions include formalizing the GRATP problem in adaptive collaboration and presenting a solution to it. The effectiveness of the proposed method is verified by experiments.

This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p -norm ( p ≥ 2 ). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M -matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

This article investigates finite-time stabilization of competitive neural networks with discrete time-varying delays (DCNNs). By virtue of comparison strategies and inequality techniques, finite-time stabilization of the underlying DCNNs is analyzed by designing a discontinuous state feedback controller, which simplifies the controller design and proof processes of some existing results. Meanwhile, global exponential stabilization of the DCNNs is provided under a continuous state feedback controller. In addition, global exponential stability of the DCNNs is shown as an M-matrix, which contains some published outcomes as special cases. Finally, three examples are given to illuminate the validity of the theories.

This article investigates global exponential stabilization (GES) of Takagi–Sugeno (T–S) fuzzy memristive neural networks with multiple time-varying delays (DFMNNs) via intermittent control strategy. By resorting to differential inclusion theory, comparison means, and inequality techniques, some results are developed to ensure GES of the underlying DFMNNs via a fuzzy intermittent state feedback control law within the sense of Filippov. The outcome is generalized to GES of FMNNs with infinite distributed time delays. Additionally, the global exponential stability of FMNNs with discrete time-varying delays is explored in terms of 1-norm. The derived conditions herein contain certain existing ones as special cases. Finally, three examples are presented to illuminate the validness of the outcomes.

Task assignment is a critical part of human-robot collaboration. The traditional task assignment methods in the intuitionistic fuzzy (IF) environment only consider two options, i.e., the task is executed by humans or machines. However, some tasks may need to be completed by humans and machines working together, such as mecha. Therefore, we propose a human-robot task assignment method under the IF environment based on three-way decision (3WD) theory in this paper. By extending the traditional two-way assignment mode into the three-way one, our method can flexibly deal with the concerned situations. At first, the appropriateness of assigning each task to the machine is calculated by IF TOPSIS. When ranking the IF evaluation values, the decision maker's attitude toward uncertainty is considered. Then the appropriateness served as conditional probability and a cost-sensitive IF 3WD model is constructed to complete the assignment. Furthermore, we construct algorithms to deal with dynamic situations. Finally, a numerical example illustrates the feasibility of the proposed method.

This article investigates the global exponential stabilization (GES) of inertial memristive neural networks with discrete and distributed time-varying delays (DIMNNs). By introducing the inertial term into memristive neural networks (MNNs), DIMNNs are formulated as the second-order differential equations with discontinuous right-hand sides. Via a variable transformation, the initial DIMNNs are rewritten as the first-order differential equations. By exploiting the theories of differential inclusion, inequality techniques, and the comparison strategy, the p th moment GES ( p ≥ 1 ) of the addressed DIMNNs is presented in terms of algebraic inequalities within the sense of Filippov, which enriches and extends some published results. In addition, the global exponential stability of MNNs is also performed in the form of an M-matrix, which contains some existing ones as special cases. Finally, two simulations are carried out to validate the correctness of the theories, and an application is developed in pseudorandom number generation.

This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays (DMNNs). By means of inequality techniques, theories of the M-matrix, and the comparison strategy, the Lagrange exponential stability of the underlying DMNNs is considered in the sense of Filippov, and the globally exponentially attractive set is estimated through employing the M-matrix and external input. Especially, when the external input is not concerned, the Lyapunov exponential stability of the corresponding DMNNs is developed immediately in the form of an M-matrix, which contains some published outcomes as special cases. Furthermore, by constructing an M-matrix-based differential system, the Lyapunov exponential stability of the DMNNs is studied, which is less conservative than some existing ones. Finally, three simulation examples are carried out to examine the validness of the theories.

This paper focuses on Lagrange exponential stability and finite-time stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks with discrete and distributed time-varying delays (DFMNNs). By resorting to theories of differential inclusions and the comparison strategy, an algebraic condition is developed to confirm Lagrange exponential stability of the underlying DFMNNs in Filippov's sense, and the exponentially attractive set is estimated. When external input is not considered, global exponential stability of DFMNNs is derived directly, which includes some existing ones as special cases. Furthermore, finite-time stabilization of the addressed DFMNNs is analyzed by exploiting inequality techniques and the comparison approach via designing a nonlinear state feedback controller. The boundedness assumption of activation functions is removed herein. Finally, two simulations are presented to demonstrate the validness of the outcomes, and an application is performed in pseudorandom number generation.

This paper investigates the φ-type stability and robust stability for a general class of stochastic reaction-diffusion neural networks (SRDNNs) with Dirichlet boundary conditions, infinite discrete time-varying delays, and infinite continuously distributed delays. By virtue of inequality techniques, properties of M-matrix, and theories of stochastic analysis, several sufficient criteria are obtained to guarantee the almost sure φ-type stability, pth moment φ-type stability, and φ-type robust stability of the underlying SRDNNs with hybrid unbounded time delays. With appropriate choices of the function φ, the φ-type stability reduces to the exponential stability, polynomial stability, and logarithmic stability. Additionally, the developed results herein include some existing ones as special cases. A numerical simulation is performed to substantiate the effectiveness and superiority of the theoretical analysis.

This paper investigates the stability and stabilization of Takagi-Sugeno (T-S) fuzzy systems with discrete and distributed time-varying delays. First, pth moment global exponential stability (p≥1) of the addressed delayed fuzzy systems is considered by virtue of the comparison approach and inequality techniques. The developed algebraic criteria include some existing outcomes as special cases. Second, global exponential stabilization of the underlying delayed fuzzy systems is performed under a fuzzy state feedback controller. Third, considering that only a few studies have been concerned with finite-time stabilization of T-S fuzzy systems, by employing the comparison strategy and a nonlinear controller, finite-time stabilization of the nominated delayed fuzzy systems is presented. The result obtained herein establishes a general theoretical framework to analyze the finite-time behavior of delayed T-S fuzzy systems. Finally, simulation examples are conducted to illustrate the validity of the results.

This paper is concerned with exponential stabilization for a class of Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with unbounded discrete and distributed time-varying delays. Under the framework of Filippov solutions, algebraic criteria are established to guarantee exponential stabilization of the addressed FMNNs with hybrid unbounded time delays via designing a fuzzy state feedback controller by exploiting inequality techniques, calculus theorems, and theories of fuzzy sets. The obtained results in this paper enhance and generalize some existing ones. Meanwhile, a general theoretical framework is proposed to investigate the dynamical behaviors of various neural networks with mixed infinite time delays. Finally, two simulation examples are performed to illustrate the validity of the derived outcomes.

This paper considers the tracking synchronization problem for a class of coupled reaction-diffusion neural networks (CRDNNs) with undirected topology. For the case where the tracking trajectory has identical individual dynamic as that of the network nodes, the edge-based and vertex-based adaptive strategies on coupling strengths as well as adaptive controllers, which demand merely the local neighbor information, are proposed to synchronize the CRDNNs to the tracking trajectory. To reduce the control costs, an adaptive pinning control technique is employed. For the case where the tracking trajectory has different individual dynamic from that of the network nodes, the vertex-based adaptive strategy is proposed to drive the synchronization error to a relatively small area, which is adjustable according to the parameters of the adaptive strategy. This kind of adaptive design can enhance the robustness of the network against the external disturbance posed on the tracking trajectory. The obtained theoretical results are verified by two representative examples.

In this paper, stabilization for a class of Takagi-Sugeno (T-S) fuzzy memristive neural networks (FMNNs) with mixed time delays is investigated. By virtue of theories of differential equations with discontinuous right-hand sides, inequality techniques, and the comparison method, an algebraic criterion is derived to stabilize the addressed FMNNs with bounded discrete and distributed time delays via a designed fuzzy state feedback controller in Filippov's sense. The result can be reinforced to stabilize FMNNs with unbounded discrete time delays. Meanwhile, exponential stabilization of FMNNs with bounded discrete time delays and unbounded continuously distributed delays is also discussed. FMNNs in this study are general since fuzzy logics and hybrid time delays are all considered, and the obtained conditions enhance and extend some existing ones. Finally, four numerical simulations are carried out to substantiate the efficiency and merits of developed theoretical results.

This paper investigates the synchronization issue of coupled reaction-diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results.

Collaboration is complex. To solve a problem occurring in collaboration, using computers, we must first define and specify the problem. This paper presents a challenging problem in collaboration, called group role assignment with cooperation and conflict factors (GRACCFs). This problem's solution aims at creating a high-performance group by role assignment with consideration of cooperation and conflicts between agents. The contribution of this paper is the formalization of the proposed problem, a confirmation of the complexity of the problem, a practical solution that uses the IBM ILOG CPLEX optimization package (ILOG), a verification of the benefits of solving the GRACCF problem by simulations and a practical way of collecting the required factors to support decision makers in solving such a problem within a real-world scenario. Experiments are used to verify the efficiency of the proposed ILOG solution.

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

This paper deals with the global exponential stability for delayed recurrent neural networks (DRNNs). By constructing an augmented Lyapunov-Krasovskii functional and adopting the reciprocally convex combination approach and Wirtinger-based integral inequality, delay-dependent global exponential stability criteria are derived in terms of linear matrix inequalities. Meanwhile, a general and effective method on global exponential stability analysis for DRNNs is given through a lemma, where the exponential convergence rate can be estimated. With this lemma, some global asymptotic stability criteria of DRNNs acquired in previous studies can be generalized to global exponential stability ones. Finally, a frequently utilized numerical example is carried out to illustrate the effectiveness and merits of the proposed theoretical results.

Adaptive collaboration (AC) is essential for group performance optimization in collaborative systems. This paper begins by introducing AC within the context of solving a real-world problem. Next, AC problems are formalized based on the environment-class, agent, role, group, and object (E-CARGO) model. Three algorithms are proposed for solving AC problems. They are based on three different scenarios: 1) the current group state (GS); 2) the GS after a specific period; and 3) the GS throughout the collaboration. More complex AC problems and their solutions are then investigated. Derived from the above-mentioned algorithms, two additional algorithms are presented. They consider reassignment costs. Experiments are developed to analyze the performance of each proposed algorithm. Results indicate that the proposed algorithms perform better than static collaboration where there are no reassignment costs. If reassignment costs exist, we also provide a way of determining whether to adopt an AC approach. This paper provides insights into the AC process and its effectiveness in various scenarios.

In this paper, we investigate maximum likelihood equalization (MLE) for a large-scale antenna (LSA) system with single-carrier (SC) modulation over Rician fading channels. Orthogonal frequency division multiplexing (OFDM) is usually used to deal with frequency selectivity of wireless channels. However, for a wireless system with large-scale antennas in a Rayleigh fading channel, by combining the received signals through a matched filter (MF), the frequency selective channel can be converted into a frequency flat channel. As a result, SC modulation can be used directly with a simple one-tap equalizer. In a Rician fading channel, however, the line-of-sight (LOS) path will cause mutliuser-interference (MUI), which cannot be mitigated through MF. As a result, the simple one-tap equalizer leads to an error-floor when the signal-to-noise ratio (SNR) is large. In this paper, MLE is used to improve system performance through multiuser detection. From both theoretical analysis and simulation results, the proposed approach can eliminate the error floor and outperform existing approach.

Group role assignment with a flexible formation (GRAFF) is essential for group performance optimization in collaborative systems. In this paper, problems of GRAFF are formalized based on the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model. Then, based on group role assignment (GRA) and linear programming (LP), two algorithms are proposed. Experiments are developed to analyze the performance of each proposed algorithm. Results indicate that the proposed algorithms are effective and the linear programming-based algorithm implemented with the IBM ILOG CPLEX package is the most efficient way to solve problems of GRAFF.