Stochastic stability of fuzzy Markovian jump neural networks by multiple integral approach

Author:

Zheng Cheng-De

Abstract

Purpose The purpose of this paper is to develop a methodology for the stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square. Design/methodology/approach The authors perform Briat Lemma, multiple integral approach and linear convex combination technique to investigate a class of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay. New sufficient criterion is established by linear matrix inequalities conditions. Findings It turns out that the obtained methods are easy to be verified and result in less conservative conditions than the existing literature. Two examples show the effectiveness of the proposed results. Originality/value The novelty of the proposed approach lies in establishing a new Wirtinger-based integral inequality and the use of the Lyapunov functional method, Briat Lemma, multiple integral approach and linear convex combination technique for stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.

Publisher

Emerald

Subject

General Computer Science

Reference29 articles.

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3. Convergence and equivalence results for the Jensen’s inequality-application to time-delay and sampled-data systems;IEEE Transactions on Automatic Control,2011

4. Dynamics analysis of impulsive stochastic Cohen-Grossberg neural networks with Markovian jumping and mixed time delays;Neurocomputing,2009

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