Global exponential stability of periodic solution of neural network with variable coefficients and time-varying delays

被引：23

作者：

Tan, Mingjia ^{[1
]}

Tan, Yong ^{[1
]}

机构：

[1] Hubei Inst Nationalities, Sch Informat Engn, Enshi 445000, Hubei, Peoples R China

来源：

APPLIED MATHEMATICAL MODELLING | 2009年 / 33卷 / 01期

关键词：

Periodic solution; Global exponential stability; Coincidence degree; Neural networks; Delays; EXISTENCE; DYNAMICS;

D O I：

10.1016/j.apm.2007.11.010

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

By using the continuation theorem of Mawhin's coincidence degree theory and some inequality techniques, some new sufficient conditions are obtained ensuring existence and global exponential stability of periodic solution of neural networks with variable coefficients and time-varying delays. These results are helpful to design globally exponentially stable and oscillatory neural networks. Finally, the validity and performance of the obtained results are illustrated by two examples. (C) 2007 Elsevier Inc. All rights reserved.

引用

页码：373 / 385

页数：13

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