Title: Exponential stability analysis for the switched stochastic Hopfield neural networks with time-varying delays

Authors: Huimin Xiao; Chunhua Wang

Addresses: Department of Mathematics and Information Science, Henan University of Economics and Law, Zhengzhou, 450002, China ' School of Mathematics Science, Ocean University of China, Qingdao, 266160, China

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.

Keywords: switched stochastic Hopfield neural networks; exponential stability; time-varying delays; switching law; stability analysis.

DOI: 10.1504/IJAMECHS.2012.051567

International Journal of Advanced Mechatronic Systems, 2012 Vol.4 No.3/4, pp.159 - 165

Published online: 30 Aug 2014 *

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