Exponential stability analysis for the switched stochastic hopfield neural networks with time-varying delays

Abstract

In this paper, the robust exponential stability analysis is considered for a class of switched stochastic Hopfield neural systems with parameter uncertainties and stochastic perturbations. The parameter uncertainties are assumed to be norm bounded. Firstly, based on Lyapunov-Krasovskii functional and linear matrix inequality (LMI) tools, by employing multiple Lyapunov function techniques, a delay-dependent sufficient condition is derived for the switched stochastic neural networks with time-varying delays under an appropriate switching law. Secondly, the sufficient criteria is given to guarantee the uncertain switched stochastic Hopfield neural systems to be mean-square exponentially stable for all admissible parametric uncertainties. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theory.