This paper investigates the anti-synchronization control problem for a class of Memristor-based Neural Networks with time-delay and semi-Markov jump parameters. Firstly, to further effectively utilize the network resources, a novel Intermittent Discrete Dynamic Event-triggered (IDDET) scheme is introduced, where the dynamical update law of the IDDET scheme is designed to be related to the current sampling state. Secondly, by fully considering the information of jump parameters, time-delay, sampling period, and interaction of the current and past states, a general common Lyapunov functional is constructed. Then, with the virtue of inequalities analysis technique and quadratic polynomial negative definite lemma, a new less conservative criterion guaranteeing anti-synchronization for the underlying master-slave systems is derived in the form of Linear Matrix Inequalities (LMIs). In the end, the validity of our results is illustrated through a numerical example.

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.

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.

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.

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.

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 event-triggered stochastic synchronization problem for neutral-type semi-Markovian jump (SMJ) neural networks with partial mode-dependent additive time-varying delays (ATDs), where the SMJ parameters in two ATDs are considered to be not completely the same as the one in the connection weight matrices of the systems. Different from the weak infinitesimal operator of multi-Markov processes, a new one for the double semi-Markovian processes (SMPs) is first proposed. To reduce the conservative of the stability criteria, a generalized reciprocally convex combination inequality (RCCI) is established by the virtue of an interesting technique. Then, based on an eligible stochastic Lyapunov-Krasovski functional, three novel stability criteria for the studied systems are derived by employing the new RCCI and combining with a well-designed event-triggered control scheme. Finally, three numerical examples and one practical engineering example are presented to show the validity of our methods.