This paper studies the problem of synchronization for a class of stochastic complex dynamical networks. It designs for the first time a distributed impulsive protocol based on pinning control that involves a constant signal transmission delay to tackle synchronization issues of such networks. Novel criteria on network synchronization are established by employing a time-dependent Lyapunov functional and a mathematical induction approach, where information on the state variables themselves and their neighbors is sufficiently utilized. Moreover, it is shown that the frequency of impulsive occurrence, impulsive input delays, stochastic perturbations, and the feedback control strength can significantly affect the synchronization performance. Numerical simulations are given to illustrate the effectiveness of the derived theoretical results.

This paper deals with the issue of sampled-data stabilization for T-S fuzzy systems (TSFSs) with state delays and nonuniform sampling. First, a fuzzy membership function (FMFs)-dependent approach is proposed, which uses information not only on both the delayed state and actual sampling pattern but also on the FMFs. Second, the inner sampling interval is split into flexible terminals, and a novel FMFs-dependent Lyapunov-Krasovskii functional (LKF) is constructed. Meanwhile, a fuzzy sampled-data controller (FSDC) with a switched topology is designed to solve the tricky issue on the estimation of FMFs-dependent terms. Then, based on the LKF methodology, the extended Wirtinger's inequality, and an improved reciprocally convex combination strategy, some relaxed criteria with both a larger sampling period and upper bound of time delays for achieving the stabilization of TSFSs are derived. Two numerical examples are presented to demonstrate the superiority and applicability of the proposed scheme.

This paper investigates the issue of sampled-data stabilization for Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with time-varying delay. First, the concerned FMNNs are transformed into the tractable fuzzy NNs based on the excitatory and inhibitory of memristive synaptic weights using a new convex combination technique. Meanwhile, a switched fuzzy sampled-data controller is employed for the first time to tackle stability problems related to FMNNs. Then, the novel stabilization criteria of the FMNNs are established using the fuzzy membership functions (FMFs)-dependent Lyapunov-Krasovskii functional. This sufficiently utilizes information from not only the delayed state and the actual sampling pattern but also the FMFs. Two simulation examples are presented to demonstrate the feasibility and validity of the proposed method.

This paper studies the problems of stability and stabilization of a class of chaotic systems (CSs) with Takagi-Sugeno fuzzy model and nonuniform sampling. It designs a fuzzy sampled-data protocol based on a switched idea to tackle the stability issue of such systems. Novel criteria on stabilization of CSs are established by employing a fuzzy membership function (FMF)-dependent Lyapunov functional and the information of the time derivative of FMFs, which significantly utilize the available characteristics of the actual sampling pattern and FMFs simultaneously. Unlike the existing works, a larger sampling interval is obtained by this new approach. A simulation example on chaotic Rossler's system is employed to demonstrate the superiority and reduced conservatism of the proposed method.