New global exponential stability result to a general Cohen-Grossberg neural networks with multiple delays

被引：20

作者：

Zhang, Zhengqiu ^{[1
]}

Zhang, Taipo ^{[1
]}

Huang, Shengye ^{[2
]}

Xiao, Ping ^{[1
]}

机构：

[1] Hunan Univ, Coll Math, Changsha 410082, Hunan, Peoples R China

[2] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China

来源：

NONLINEAR DYNAMICS | 2012年 / 67卷 / 04期

关键词：

The existence of an equilibrium point; The general Cohen-Grossberg neural networks with multiple delays; Global exponential stability; Linear matrix inequality technique; Degree theory; Lyapunov functional; TIME-VARYING DELAYS; DISTRIBUTED DELAYS; ROBUST STABILITY; ASYMPTOTIC STABILITY; PERIODIC-SOLUTIONS; CRITERIA; PARAMETERS; DISCRETE;

D O I：

10.1007/s11071-011-0156-6

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In this paper, we first discuss the existence of an equilibrium point to a general Cohen-Grossberg neural networks with multiple delays by means of using degree theory and linear matrix inequality (LMI) technique. Then by applying the existence result of an equilibrium point, linear matrix inequality technique and constructing a Lyapunov functional, we study the global exponential stability of equilibrium solution to the Cohen-Grossberg neural networks. Compared with known results, our results of global exponential stability of equilibrium point are new. In our results, the hypothesis for differentiability in existing papers on the behaved functions is removed and the hypotheses for boundedness and monotonicity in existing papers on the activation functions are also removed.

引用

页码：2419 / 2432

页数：14

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