Exponential stability analysis for the switched stochastic Hopfield neural networks with time-varying delays
by Huimin Xiao; Chunhua Wang
International Journal of Advanced Mechatronic Systems (IJAMECHS), Vol. 4, No. 3/4, 2012

Abstract: In this paper, the robust exponential stability analysis is investigated 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 means of 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 are 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.

Online publication date: Sat, 30-Aug-2014

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