Exponential convergence estimates for analog neural networks with multiple delays

被引：0
|
作者：
Chu, TG [1 ]
Wang, ZL [1 ]
Wang, L [1 ]
机构：
[1] Peking Univ, Ctr Syst & Control, Dept Engn Sci & Mech, Beijing 100871, Peoples R China
来源：
PROCEEDINGS OF THE 40TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5 | 2001年
关键词：
neural networks; multiple time delays; global exponential convergence; comparison principle;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A componentwise estimate of exponential convergence is obtained for a class of neural networks with multiple delays by using a method based on a comparison principle of delay differential systems. The method is simple and straightforward in analysis, without resorting to any Lyapunov functionals. The result shows explicitly the effect of time delays on exponential decay rate of the networks and is of practical significance for designing fast and stable neural networks. Some existing results for Hopfield model and DCNN models via Lyapunov functional method are found to be special cases of the present result.
引用
收藏
页码：2715 / 2719
页数：5
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