Stability criteria for Cohen-Grossberg neural networks with mixed delays and inverse Lipschitz neuron activations

被引：9
|
作者：
Zheng, Cheng-De [1 ]
Gong, Chao-Ke [1 ]
Wang, Zhanshan [2 ]
机构：
[1] Dalian Jiaotong Univ, Sch Sci, Dalian 116028, Peoples R China
[2] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2012年 / 349卷 / 09期
基金：
中国国家自然科学基金;
关键词：
ROBUST EXPONENTIAL STABILITY; GLOBAL ASYMPTOTIC STABILITY;
D O I：
10.1016/j.jfranklin.2012.08.001
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, by using topological degree theory and Lyapunov-Krasovskii function method, the problem of stability is investigated for a class of mixed-delayed Cohen-Grossberg neural networks with inverse Lipschitz neuron activations and nonsmooth behaved functions. Several novel delay-dependent sufficient conditions are established towards the existence, uniqueness and global exponential stability of the equilibrium point, which are shown in terms of linear matrix inequalities. Besides, for the case of the activation function satisfying not only the inverse Lipschitz conditions but also the Lipschitz conditions, two criteria are derived by virtue of homeomorphism mapping principle, free-weighting matrix method and Cauchy-Schwarz inequality, which generalize some previous results. Finally, two examples with their simulations are given to show the effectiveness of the theoretical results. (c) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2903 / 2924
页数：22
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