Necessary and sufficient condition for the absolute exponential stability of a class of neural networks with finite delay

被引：19

作者：

Huang, TW

Cao, JD

Li, CD

机构：

[1] Texas A&M Univ, Qatar Fdn, Doha, Qatar

[2] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China

[3] Chongqing Univ, Dept Comp Sci & Engn, Chongqing 400030, Peoples R China

来源：

PHYSICS LETTERS A | 2006年 / 352卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

neural networks; finite delay; exponential stability; necessary and sufficient condition; M-matrix; absolute exponential stability;

D O I：

10.1016/j.physleta.2005.11.038

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

In this Letter, a necessary and sufficient condition is established for ascertaining the absolute exponential stability for a class of finite delayed neural networks with connection matrices A having nonnegative off-diagonal elements, delayed feedback matrix A(tau) having nonnegative elements and strictly increasing and Lipschitz activation functions. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：94 / 98

页数：5

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