Robust global stability of discrete-time recurrent neural networks

被引：3

作者：

Mahmoud, M. S. ^{[1
]}

机构：

[1] King Fahd Univ Petr & Minerals, Dept Syst Engn, Dhahran 31261, Saudi Arabia

来源：

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING | 2009年 / 223卷 / I8期

关键词：

discrete-time systems; recurrent neural networks; time-varying delays; delay-range-dependent stability; LMIs; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; CRITERIA;

D O I：

10.1243/09596518JSCE822

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 establishes new delay-range-dependent, robust global stability for a class of discrete-time recurrent neural networks with interval time-varying delays and norm-bounded time-varying parameter uncertainties. A new Lyapunov-Krasovskii functional is constructed to exhibit the delay-dependent dynamics and compensate for the enlarged time-span. The developed stability method eliminates the need for over bounding and utilizes a smaller number of linear matrix inequality (LMI) decision variables. New and less conservative solutions to the global stability problem are provided in terms of feasibility testing of new parametrized LMIs. Numerical examples are presented to illustrate the effectiveness of the developed technique.

引用

页码：1045 / 1053

页数：9

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