This paper investigates the resilient dynamic feedback control problem for fuzzy multi-rate systems (FMRS) subject to a novel important-data-based attack (IDBA). In practical engineering systems, physical limitations such as sensor aging, quantization effects, and unstable power supplies often cause discrepancies between measurement outputs and actual system states, leading to multi-rate sampling behaviors. To accurately characterize such sampling inconsistencies, a nonhomogeneous Markov sampling model is introduced, which provides a more realistic description and reduces conservatism compared with traditional homogeneous models. Motivated by the need for stealthy yet destructive cyber attacks, this paper further proposes an IDBA scheme, in which the adversary selectively identifies and disrupts only the most critical data nodes based on their instantaneous impact on system behavior. To mitigate the severe disruptions caused by IDBA, a resilient dynamic output feedback controller is designed, thereby enhancing adaptability under adverse network conditions. Simulation studies, including both numerical and practical examples, demonstrate that the proposed control framework effectively preserves system stability and achieves superior performance in the presence of IDBA.

The study focuses on the problem of switching event-triggered protocol control for fuzzy singularly perturbed systems under random sampling. The non-uniform sampling of the model is characterized by introducing a random variable obeying the Markov process. Nextly, to reduce the network transmission burden, a novel switching event-triggered protocol is proposed, which can dynamically adjust the triggering parameters based on the time interval between the current sampling instant and the previous sampling instant. Meanwhile, a switching fuzzy event triggered controller is devised, by jointly triggering state information. Additionally, a set of sufficient conditions is derived to ensure the finite-time stability of the closed-loop system. The effectiveness and advantages of the proposed methodology are validated through both a numerical simulation and a practical example, demonstrating its feasibility and superiority.

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.

In this paper deal with the problem of asymptotic stability for a class of uncertain stochastic neural networks with discrete-time and unbounded distributed delays. The sufficient conditions to guarantee the robust global asymptotic stability in mean square of an equilibrium solution are given. One example is also given to demonstrate our results.