Global exponential stability for uncertain bidirectional associative memory neural networks with multiple time-varying delays via LMI approach

被引：9

作者：

Gau, Ruey-Shyan ^{[2
]}

Hsieh, Jer-Guang ^{[2
]}

Lien, Chang-Hua ^{[1
]}

机构：

[1] Natl Kaohsiung Marine Univ, Dept Marine Engn, Kaohsiung 811, Taiwan

[2] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 804, Taiwan

来源：

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS | 2008年 / 36卷 / 04期

关键词：

delayed bidirectional associative memory neural networks; linear matrix inequality; global exponential stability; Lyapunov approach;

D O I：

10.1002/cta.449

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The global exponential stability for uncertain delayed bidirectional associative memory neural networks (DBAMNN) with multiple time-varying delays is considered in this paper. Delay-dependent criteria are proposed to guarantee the robust stability of DBAMNN via linear matrix inequality approach. Two classes of system uncertainties are investigated in this paper. Some numerical examples are given to illustrate the effectiveness of our results. From the numerical simulations, significant improvement over the recent results can be observed. Copyright (C) 2007 John Wiley & Sons, Ltd.

引用

页码：451 / 471

页数：21

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