Robust stability analysis for discrete-time stochastic neural networks systems with time-varying delays

被引：60
|
作者：
Luo, Mengzhuo [1 ]
Zhong, Shouming [1 ]
Wang, Rongjun [1 ]
Kang, Wei [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Appl Math, Chengdu 610054, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2009年 / 209卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Discrete-time stochastic neural networks; Linear matrix inequality (LMI); Exponential stability; Delay-dependent criteria; Lyapunov-Krasovskii functional; Time-varying delays; EXPONENTIAL STABILITY; STATE ESTIMATION; SYNCHRONIZATION;
D O I：
10.1016/j.amc.2008.12.084
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, the mean square exponential stability is investigated for a class of discrete-time stochastic neural networks with time-varying delays and norm-bounded uncertainties. Based on Lyapunov stability theory and stochastic approaches, delay-dependent criteria are derived to ensure the robust exponential stability in the mean square for the addressed system. Meantime, by using the numerically efficient Matlab LMI Toolbox, a example is presented to show the usefulness of the derived LMI-based stability condition. Crown Copyright (C) 2008 Published by Elsevier Inc. All rights reserved.
引用
收藏
页码：305 / 313
页数：9
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