Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching | Zendy

Wuneng Zhou | Zendy; Xueqing Yang | Zendy; Jun Yang | Zendy; Anding Dai | Zendy; Huashan Liu | Zendy

Open Access

Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching

Author(s) -

Wuneng Zhou,

Xueqing Yang,

Jun Yang,

Anding Dai,

Huashan Liu

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/479084

Subject(s) - artificial neural network , perturbation (astronomy) , control theory (sociology) , synchronization (alternating current) , mathematics , exponential stability , markov process , type (biology) , simultaneous perturbation stochastic approximation , lyapunov function , stochastic neural network , matlab , stochastic differential equation , computer science , stochastic process , recurrent neural network , topology (electrical circuits) , nonlinear system , control (management) , physics , operating system , ecology , statistics , quantum mechanics , combinatorics , machine learning , artificial intelligence , biology

The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper

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