Exponential stability for stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays

被引：26

作者：

Du, Yuanhua ^{[1
]}

Zhong, Shouming ^{[1
]}

Zhou, Nan ^{[2
]}

Shi, Kaibo ^{[2
]}

Cheng, Jun ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China

来源：

NEUROCOMPUTING | 2014年 / 127卷

基金：

中国国家自然科学基金;

关键词：

Cohen-Grossberg BAM neural networks; Exponential stability; Linear matrix inequality; Stochastic effect; Time-varying delays; BIDIRECTIONAL ASSOCIATIVE MEMORIES; ROBUST STABILITY;

D O I：

10.1016/j.neucom.2013.08.028

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper considers the issue of exponential stability analysis for stochastic Cohen-Grossberg BAM (SCGBAM) neural networks with discrete and distributed time-varying delays. The exponential stability criteria are proposed by applying stochastic analysis theory and establishing a new Lyapunov-Krasovskii functional. A set of novel sufficient conditions is obtained to guarantee the exponential stability of stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays. The several exponential stability criteria proposed in this paper are simpler and effective. Finally, two numerical examples are provided to demonstrate the low conservatism and effectiveness of the proposed results. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：144 / 151

页数：8

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