Globally exponentially robust stability and periodicity of delayed neural networks

被引：137

作者：

Cao, J ^{[1
]}

Chen, TP

机构：

[1] SE Univ, Dept Math, Nanjing 210096, Peoples R China

[2] Fudan Univ, Lab Math & Nonlinear Sci, Shanghai 200433, Peoples R China

来源：

CHAOS SOLITONS & FRACTALS | 2004年 / 22卷 / 04期

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1016/j.chaos.2004.03.019

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, a new concept of robust periodicity is introduced, and problem of robust stability and robust periodicity is discussed for delayed neural networks. Several sufficient conditions are derived for globally exponentially robust stability and robust periodicity of delayed neural networks based Lyapunov method. These results improve and extend those given in the earlier references. (C) 2004 Elsevier Ltd. All rights reserved.

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页码：957 / 963

页数：7

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