New global asymptotic stability of discrete-time recurrent neural networks with multiple time-varying delays in the leakage term and impulsive effects

This paper investigates the problem of discrete-time stochastic recurrent neural networks with multiple time-varying delays in the leakage terms and impulses. A new set of sufficient conditions are obtained by constructing an appropriate Lyapunov-Krasovskii functional combining with linear matrix in...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 214; pp. 420 - 429
Main Authors Balasundaram, K., Raja, R., Zhu, Quanxin, Chandrasekaran, S., Zhou, Hongwei
Format Journal Article
LanguageEnglish
Published Elsevier B.V 19.11.2016
Subjects
Asymptotic stability
Delay-dependent
Impulse
Leakage delay
Linear matrix inequality
Recurrent neural network
Time-varying delay
Leakage delay
Asymptotic stability
Recurrent neural network
Time-varying delay
Delay-dependent
Linear matrix inequality
Impulse
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Summary:This paper investigates the problem of discrete-time stochastic recurrent neural networks with multiple time-varying delays in the leakage terms and impulses. A new set of sufficient conditions are obtained by constructing an appropriate Lyapunov-Krasovskii functional combining with linear matrix inequality technique and free weighting matrix method. The obtained delay-dependent stability conditions are expressed in terms of linear matrix inequalities and it can be solved via some available software packages. Up to now, the asymptotic stability problem is studied for discrete-delay in the leakage terms. For the first time in our paper, we have considered distributed delays and impulses for such kind of networks. In addition, we have provided a numerical example to demonstrate the effectiveness of our obtained stability results for the theoretical section.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.06.040