Attractor and boundedness for stochastic Cohen-Grossberg neural networks with delays

被引：8

作者：

Wan, Li ^{[1
]}

Zhou, Qinghua ^{[2
]}

机构：

[1] Wuhan Text Univ, Sch Math & Comp, Wuhan 430073, Peoples R China

[2] Zhaoqing Univ, Dept Math, Zhaoqing 526061, Peoples R China

来源：

NEUROCOMPUTING | 2012年 / 79卷

基金：

中国国家自然科学基金;

关键词：

Stochastic Cohen-Grossberg neural networks; Attractor; Delays; SQUARE EXPONENTIAL STABILITY; ROBUST STABILITY; DISCRETE; CRITERIA;

D O I：

10.1016/j.neucom.2011.10.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

By employing Lyapunov method and Lasalle-type theorem, the attractor of stochastic Cohen-Grossberg neural networks (CGNN) with delays is initially investigated. Novel results and sufficient criteria on the attractor of stochastic CGNN are obtained. The almost surely asymptotic stability is a special case of our results. The boundedness of stochastic CGNN is also investigated. Finally, one example is presented to illustrate the correctness and effectiveness of our theoretical results. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：164 / 167

页数：4

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