Global exponential stabilization of delayed BAM neural networks: a matrix measure approach

被引：0

作者：

Li, Yong ^{[1
]}

Wu, Guodong ^{[1
]}

Peng, Liu ^{[1
]}

Cao, Guangming ^{[1
]}

机构：

[1] Wuhan Second Ship Design & Res Inst, Wuhan, Hubei, Peoples R China

来源：

PROCEEDINGS OF THE 2017 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTER (MACMC 2017) | 2017年 / 150卷

关键词：

global exponential stabilization; BAM neural networks; time delays; matrix measure; Lyapunov stability theory; MEASURE STRATEGIES; TIME DELAYS; SYNCHRONIZATION; BIFURCATION; STABILITY;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, global exponential stabilization of bidirectional associative memory (BAM) neural networks with time delays is investigated. Based on the Lyapunov stability theory, we present several sufficient conditions for the global exponential stability of the equilibrium point of the BAM neural networks with delays via matrix measure approach. The presented results are easy to verify and simple to implement in practice. Finally, one numerical example is given to illustrate the feasibility and effectiveness of our theoretical results.

引用

页码：260 / 267

页数：8

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