Global asymptotical stability of continuous-time delayed neural networks without global Lipschitz activation functions

被引：8
|
作者：
Tan, Yong [1 ]
Tan, Mingjia [2 ]
机构：
[1] Wuhan Polytech Univ, Sch Econ & Management, Wuhan 430023, Hubei, Peoples R China
[2] Hubei Inst Nationalities, Sch Informat Engn, Enshi 445000, Hubei, Peoples R China
来源：
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2009年 / 14卷 / 11期
关键词：
Neural networks; Delays; Global asymptotic stability; EXPONENTIAL STABILITY; EXISTENCE;
D O I：
10.1016/j.cnsns.2009.01.032
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper investigates the global asymptotic stability of equilibrium for a class of continuous-time neural networks with delays. Based on suitable Lyapunov functionals and the homeomorphism theory, some sufficient conditions for the existence and uniqueness of the equilibrium point are derived. These results extend the previously works without assuming boundedness and Lipschitz conditions of the activation functions and any symmetry of interconnections. A numerical example is also given to show the improvements of the paper. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码：3715 / 3722
页数：8
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