Stability in Lagrange sense for Cohen-Grossberg neural networks with time-varying delays and finite distributed delays

被引：50

作者：

Wang, Baoxian ^{[1
]}

Jian, Jigui ^{[1
]}

Jiang, Minghui ^{[1
]}

机构：

[1] China Three Gorges Univ, Inst Nonlinear Complex Sys, Yichang 443002, Hubei, Peoples R China

来源：

NONLINEAR ANALYSIS-HYBRID SYSTEMS | 2010年 / 4卷 / 01期

关键词：

Cohen-Grossberg neural network; Lagrange stability; Time-varying delay; Finite distributed delay; GLOBAL EXPONENTIAL STABILITY; BOUNDEDNESS;

D O I：

10.1016/j.nahs.2009.07.007

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, we study the global exponential stability in Lagrange sense for a class of Cohen-Grossberg neural networks with time-varying delays and finite distributed delays. Based on the Lyapunov stability theory, several global exponential attractive sets in which all trajectories converge are obtained. We analyze three different types of activation functions which include both bounded and unbounded activation functions. These results can also be applied to analyze monostable as well as multistable and more extensive neural networks due to making no assumptions on the number of equilibria. Meanwhile, the results obtained in this paper are more general and challenging than that of the existing references. Finally, one example is given and analyzed to verify our results. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：65 / 78

页数：14

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