Globally exponential stability of neural network with constant and variable delays

被引：7
|
作者：
Zhao, WR
Zhang, HS
机构：
[1] Harbin Inst Technol, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
[2] Wuhan Univ Technol, Dept Math, Wuhan 430070, Peoples R China
来源：
PHYSICS LETTERS A | 2006年 / 352卷 / 4-5期
基金：
中国国家自然科学基金;
关键词：
neural networks; variable delays; globally exponential stability; Lyapunov functional;
D O I：
10.1016/j.physleta.2005.12.015
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
This Letter presents new sufficient conditions of globally exponential stability of neural networks with delays. We show that these results generalize recently published globally exponential stability results. In particular, several different globally exponential stability conditions in the literatures which were proved using different Lyapunov functionals are generalized and unified by using the same Lyapunov functional and the technique of inequality of integral. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed neural networks. Those conditions are less restrictive than those given in the earlier references. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：350 / 357
页数：8
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