Stochastic switched sampled-data control for synchronization of delayed chaotic neural networks with packet dropout

摘要

This paper addresses the exponential synchronization issue of delayed chaotic neural networks (DCNNs) with control packet dropout. A novel stochastic switched sampled-data controller with time-varying sampling is developed in the frame of the zero-input strategy. First, by making full use of the available characteristics on the actual sampling pattern, a newly loop-delay-product-type Lyapunov–Krasovskii functional (LDPTLKF) is constructed via introducing a matrix-refined-function, which can reflect the information of delay variation. Second, based on the LDPTLKF and the relaxed Wirtinger-based integral inequality (RWBII), novel synchronization criteria are established to guarantee that DCNNs are synchronous exponentially when the control packet dropout occurs in a random way, which obeys certain Bernoulli distributed white noise sequences. Third, a desired sampled-data controller can be designed on account of the proposed optimization algorithm. Finally, the effectiveness and advantages of the obtained results are illustrated by two numerical examples with simulations.