Delay-dependent global robust stability criterion for neural networks with neutral-type time-varying delays

被引：6

作者：

Wang, G. ^{[1
]}

Chen, W. ^{[1
]}

Liu, Y. ^{[1
]}

机构：

[1] Hangzhou Dianzi Univ, Inst Operat Res & Cybernet, Hangzhou 310018, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING | 2010年 / 224卷 / I4期

关键词：

neutral-type time-varying delay; neural networks; global robust stability; linear matrix inequality; ASYMPTOTIC STABILITY; DISCRETE;

D O I：

10.1243/09596518JSCE996

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The problem of global robust stability for neural networks with neutral-type time-varying delays is investigated in this paper. Neutral differential equations are used to construct this class of neural networks. The time-varying delay function is bounded, and it is not required to be continuously differentiable. A new delay-dependent global stability criterion is derived based on the Lyapunov-Krasovskii functional approach. The criterion is formulated in terms of a linear matrix inequality. Numerical examples are given to illustrate the validity of the proposed stability criterion.

引用

页码：321 / 327

页数：7

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