Dynamic analysis of discrete-time BAM neural networks with stochastic perturbations and impulses

This paper addresses the problem of stability analysis for a class of uncertain discrete-time stochastic BAM neural networks with time-varying delays and impulses. In this paper, we assume that stochastic disturbances are described by the Brownian motion and jumping parameters are generated from discrete-time discrete-state homogeneous Markov process. By employing the Lyapunov–Krasovskii functional and stochastic analysis theory, a set of novel sufficient conditions are derived to guarantee the robust global exponential stability of the equilibrium point in the mean square. The obtained results are shown to be less conservative than the existing one in the literature. Note that the obtained results are formulated in terms of linear matrix inequality (LMI) that can efficiently solved by the LMI toolbox in Matlab. Numerical examples are given to show that the proposed result significantly improve the allowable upper bounds of delays over some existing results in the literature.