New Delay-Dependent Stability Results for Neural Networks With Time-Varying Delay

被引：64

作者：

Zhu, Xun-Lin ^{[1
,2
]}

Yang, Guang-Hong ^{[1
,3
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China

[2] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou 450002, Henan, Peoples R China

[3] Northeastern Univ, Minist Educ, Key Lab Integrated Automat Proc Ind, Shenyang 110004, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2008年 / 19卷 / 10期

关键词：

Delay-dependent stability; linear matrix inequalities (LMIs); neural networks (NNs);

D O I：

10.1109/TNN.2008.2002436

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies the problem of stability analysis for neural networks (NNs) with a time-varying delay. Unlike the previous works, the activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By defining a more general type of Lyapunov functionals, some new less conservative delay-dependent stability criteria are established in terms of linear matrix inequalities (LMIs). Meanwhile, the computational complexity of the newly obtained stability conditions is reduced because less variables are involved. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.

引用

页码：1783 / 1791

页数：9

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