Exponential stability and periodic solutions of delayed cellular neural networks

被引：0

作者：

曹进德 ^{[1
]}

机构：

[1] Department of Applied Mathematics, Southeast University, Nanjing 210096, China

来源：

Science in China(Series E:Technological Sciences) | 2000年 / 03期

关键词：

periodic solution; global exponential stability; delayed cellular neural networks; Lyapunov func-tional; inequality;

D O I：

暂无

中图分类号：

O178 [不等式及其他];

学科分类号：

0701 ; 070101 ;

摘要：

A set of criteria are presented for the global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov function-als, introducing many parameters qij* , rij* , qij, rij∈ R and wi>0 (i, j = 1, 2, …, n) and combining them with the elementary inequality 2ab≤a2 + b2 technique. These criteria have important significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, the results in literature are extended and improved. Two examples are given to illustrate the theory.

引用

页码：328 / 336

页数：9

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