Exponential stability of discrete-time stochastic neural networks with Markrovian jumping parameters and mode-dependent delays

Abstract

This paper deals with the exponential stability problem for a class of discrete-time stochastic neural networks (DSNNs) with mode-dependent delays and Markovian jumping parameters. Based on a new Lyapunov-Krasovskii functional and some well-known inequalities, we investigate the mean square exponential stability by assuming that stochastic disturbances are nonlinear and described by a Brownian motion, jumping parameters are derived from a discrete-time discrete-state Markov process. Moreover, by using the method that adds a zero item to a positive matrix, we get much less conservation results. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Cite this paper

@article{Wu2011ExponentialSO, title={Exponential stability of discrete-time stochastic neural networks with Markrovian jumping parameters and mode-dependent delays}, author={Mengjiao Wu and Zhuhua Lin and Quanxin Zhu and Yabo Lin and Qinghua Liang}, journal={2011 6th IEEE Conference on Industrial Electronics and Applications}, year={2011}, pages={930-935} }