New global exponential stability criteria for interval-delayed neural networks

被引：25

作者：

Su, X. ^{[1
]}

Li, Z. ^{[1
]}

Feng, Y. ^{[1
]}

Wu, L. ^{[1
]}

机构：

[1] Harbin Inst Technol, Space Control & Inertial Technol Res Ctr, Harbin 150001, Heilongjiang, Peoples R China

来源：

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING | 2011年 / 225卷 / I1期

关键词：

delay partitioning; interval cellular neural networks; time delay; global exponential stability; DEPENDENT ASYMPTOTIC STABILITY; DISCRETE; SYSTEMS;

D O I：

10.1243/09596518JSCE1066

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper is concerned with the problem of global robust exponential stability analysis for a class of interval cellular neural networks with time delay. By introducing a novel Lyapunov-Krasovslii function combined with the idea of delay fractioning, some delay-dependent conditions are derived in terms of the linear matrix inequality, which guarantee the considered interval delayed cellular neural networks to be globally exponentially stable. Moreover, the conservatism can be notably reduced as the fractioning becomes thinner. Some numerical examples are provided to demonstrate the advantages of the proposed results.

引用

页码：125 / 136

页数：12

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