Mean Square Exponential Stability of Stochastic Cohen-Grossberg Neural Networks with Unbounded Distributed Delays

被引：2

作者：

Huang, Chuangxia ^{[1
]}

Huang, Lehua ^{[1
]}

He, Yigang ^{[2
]}

机构：

[1] Changsha Univ Sci & Technol, Coll Math & Comp Sci, Changsha 410114, Hunan, Peoples R China

[2] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China

来源：

DISCRETE DYNAMICS IN NATURE AND SOCIETY | 2010年 / 2010卷

基金：

中国国家自然科学基金;

关键词：

GLOBAL STABILITY; DYNAMICS; CONVERGENCE;

D O I：

10.1155/2010/513218

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper addresses the issue of mean square exponential stability of stochastic Cohen-Grossberg neural networks (SCGNN), whose state variables are described by stochastic nonlinear integrodifferential equations. With the help of Lyapunov function, stochastic analysis technique, and inequality techniques, some novel sufficient conditions on mean square exponential stability for SCGNN are given. Furthermore, we also establish some sufficient conditions for checking exponential stability for Cohen-Grossberg neural networks with unbounded distributed delays.

引用

页数：15

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