A switching rule for exponential stability of switched recurrent neural networks with interval time-varying delay

被引：16

作者：

Rajchakit, Manlika ^{[1
]}

Niamsup, Piyapong ^{[2
,3
]}

Rajchakit, Grienggrai ^{[1
]}

机构：

[1] Maejo Univ, Fac Sci, Div Math & Stat, Chiangmai 50290, Thailand

[2] Chiang Mai Univ, Dept Math, Fac Sci, Chiang Mai 52000, Thailand

[3] CHE, Ctr Excellence Math, Bangkok 10400, Thailand

来源：

ADVANCES IN DIFFERENCE EQUATIONS | 2013年

关键词：

neural networks; switching design; exponential stability; interval time-varying delays; Lyapunov function; linear matrix inequalities; ASYMPTOTIC STABILITY; SYSTEMS; DISCRETE; STABILIZATION;

D O I：

10.1186/1687-1847-2013-44

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper studies the problem for exponential stability of switched recurrent neural networks with interval time-varying delay. The time delay is a continuous function belonging to a given interval, but not necessarily differentiable. By constructing a set of argumented Lyapunov-Krasovskii functionals combined with the Newton-Leibniz formula, a switching rule for exponential stability of switched recurrent neural networks with interval time-varying delay is designed via linear matrix inequalities, and new sufficient conditions for the exponential stability of switched recurrent neural networks with interval time-varying delay via linear matrix inequalities (LMIs) are derived. A numerical example is given to illustrate the effectiveness of the obtained result.

引用

页码：1 / 10

页数：10

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