Mean square exponential stability in high-order stochastic impulsive BAM neural networks with time-varying delays

被引：15

作者：

Cu, Haibo ^{[1
]}

机构：

[1] Xinjiang Normal Univ, Coll Math Sci, Urumqi 830054, Peoples R China

来源：

NEUROCOMPUTING | 2011年 / 74卷 / 05期

关键词：

High-order BAM neural networks; Impulsive; Stochastic; Equilibrium point; Mean square stability; LMI method; Lyapunov functional; GLOBAL ASYMPTOTIC STABILITY; P-STABILITY; EXISTENCE; CRITERIA;

D O I：

10.1016/j.neucom.2010.09.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a class of stochastic impulsive high-order BAM neural networks with time-varying delays is considered. By using Lyapunov functional method, LMI method and mathematics induction, some sufficient conditions are derived for the globally exponential stability of the equilibrium point of the neural networks in mean square. It is believed that these results are significant and useful for the design and applications of impulsive stochastic high-order BAM neural networks. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：720 / 729

页数：10

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