Further results on delay-dependent stability criteria of neural networks with time-varying delays

被引：100

作者：

Li, Tao ^{[1
]}

Guo, Lei ^{[2
]}

Sun, Changyin ^{[1
]}

Lin, Chong ^{[3
]}

机构：

[1] SE Univ, Res Inst Automat, Nanjing 210096, Peoples R China

[2] Beihang Univ, Sch Instrument Sci & Optoelect Engn, Beijing 100083, Peoples R China

[3] Qingdao Univ, Coll Automat Engn, Inst Complex Sci, Qingdao 266071, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2008年 / 19卷 / 04期

基金：

美国国家科学基金会;

关键词：

asymptotic stability; delay-dependent; linear matrix inequality (LMI); neural networks (NNs); robust stability;

D O I：

10.1109/TNN.2007.914162

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this brief paper, an augmented Lyapunov functional, which takes an integral term of state vector into account, is introduced. Owing to the functional, an improved delay-dependent asymptotic stability criterion for delayed neural networks (NNs) is derived in term of linear matrix inequalities (LMIs). It is shown that the obtained criterion can provide less conservative result than some existing ones. When linear fractional uncertainties appear in NNs, a new robust delay-dependent stability condition is also given. Numerical examples are given to demonstrate the applicability or the proposed approach.

引用

页码：726 / 730

页数：5

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