Exponential stability and periodicity of cellular neural networks with time delay

被引：14
|
作者：
Zhong, Shouming
Liu, Xinzhi
机构：
[1] Univ Waterloo, Dept Appl Mech, Fac Math, Waterloo, ON N2L 3G1, Canada
[2] Univ Elect Sci & Technol China, Sch Appl Math, Chengdu 610054, Peoples R China
来源：
MATHEMATICAL AND COMPUTER MODELLING | 2007年 / 45卷 / 9-10期
基金：
加拿大自然科学与工程研究理事会;
关键词：
delayed cellular neural networks; exponential stability; periodicity; partitioned matrices;
D O I：
10.1016/j.mcm.2006.10.004
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper investigates the problem of exponential stability and periodicity for a class of delayed cellular neural networks (DCNN's). By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions for exponential stability and periodicity are derived via constructing Lyapunov functional. Those conditions suitable are associated with some initial value and are represented by some blocks of the interconnection matrix. Two examples are discussed to illustrate the main results. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1231 / 1240
页数：10
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