Robust convergence of Cohen-Grossberg neural networks with time-varying delays

被引：7
|
作者：
Xiong, Wenjun [1 ]
Ma, Deyi [1 ]
Liang, Jinling [2 ]
机构：
[1] Three Gorges Univ, Coll Sci, Inst Nonlinear Complex Syst, Yichang 443002, Peoples R China
[2] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2009年 / 40卷 / 03期
关键词：
CONTINUOUSLY DISTRIBUTED DELAYS; STABILITY ANALYSIS; EXPONENTIAL STABILITY; HARMLESS DELAYS; DISCRETE; SYSTEMS; CRITERION;
D O I：
10.1016/j.chaos.2007.08.072
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, robust convergence is studied for the Cohen-Grossberg neural networks (CGNNs) with time-varying delays. By applying the differential inequality and the Lyapunov method, some delay-independent conditions are derived ensuring the robust CGNNs to converge, globally, uniformly and exponentially, to a ball in the state space with a pre-specified convergence rate. Finally, the effectiveness of our results are verified by an illustrative example. (C) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1176 / 1184
页数：9
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