A graph-theoretic approach to exponential stability of BAM neural networks with delays and reaction-diffusion

被引：2

作者：

Su, Huan ^{[1
]}

He, Zhifang ^{[1
]}

Zhao, Yuwei ^{[1
]}

Ding, Xiaohua ^{[1
]}

机构：

[1] Harbin Inst Technol Weihai, Dept Math, Weihai 264209, Shandong, Peoples R China

来源：

APPLICABLE ANALYSIS | 2015年 / 94卷 / 10期

关键词：

BAM neural networks; time-varying delays; reaction-diffusion; exponential stability; graph theory; 94C15; 34D20; TIME-VARYING DELAYS; ASSOCIATIVE MEMORY NETWORKS; DISTRIBUTED DELAYS; TERMS; CRITERIA; EXISTENCE;

D O I：

10.1080/00036811.2014.964690

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper deals with the problem of global exponential stability for bidirectional associate memory (BAM) neural networks with time-varying delays and reaction-diffusion terms. By using some inequality techniques, graph theory as well as Lyapunov stability theory, a systematic method of constructing a global Lyapunov function for BAM neural networks with time-varying delays and reaction-diffusion terms is provided. Furthermore, two different kinds of sufficient principles are derived to guarantee the exponential stability of BAM neural networks. Finally, a numerical example is carried out to demonstrate the effectiveness and applicability of the theoretical results.

引用

页码：2037 / 2056

页数：20

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