Globally exponential stability conditions for cellular neural networks with time-varying delays

被引：119
作者：
Zhou, DM [1 ]
Cao, JD [1 ]
机构：
[1] Yunnan Univ, Information Coll, Kunming 650091, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2002年 / 131卷 / 2-3期
关键词：
cellular neural network; time-varying delay; Lyapunov function; global exponential stability;
D O I：
10.1016/S0096-3003(01)00162-X
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, the problems of global exponential stability for cellular neural networks (CNN) with time-varying delays are studied. Several sufficient conditions guaranteeing the network's global exponential stability are established. These results can easily be used to design and verify globally stable networks. Furthermore, the results presented here are independent of the form of specific delays and have important significance in both theory and applications. (C) 2002 Elsevier Science Inc. All rights reserved.
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页码：487 / 496
页数：10
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