Global exponential stability for delayed cellular neural networks and estimate of exponential convergence rate

被引：0
|
作者：
Zhang Qiang
Advanced Design Technology Center
School of Management
机构：
来源：
JournalofSystemsEngineeringandElectronics | 2004年 / 03期
关键词：
global exponential stability; convergence rate; cellular neural networks with delay; delay differential inequality;
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.
引用
收藏
页码：344 / 349
页数：6
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