Global stability results of discrete recurrent neural networks with interval delays

被引：4

作者：

Mahmoud, Magdi S. ^{[1
]}

Al-Sunni, Fouad M. ^{[1
]}

机构：

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

来源：

IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION | 2012年 / 29卷 / 02期

关键词：

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

D O I：

10.1093/imamci/dnr034

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 improved global exponential stability method for a class of discrete-time recurrent neural networks with interval time-varying delays and norm-bounded time-varying parameter uncertainties. The method is robust and delay range dependent. It is derived based on a new Lyapunov-Krasovskii functional constructed to exhibit the delay-dependent dynamics and to compensate for the enlarged time span. The developed stability method eliminates the need for over bounding and utilizes 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 parameterized LMIs. Numerical examples are presented to illustrate the effectiveness of the developed technique.

引用

页码：199 / 213

页数：15

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