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
收藏
页码：94 / 98
页数：5
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