Dynamical Behaviors of the Stochastic Hopfield Neural Networks with Mixed Time Delays | Zendy

Li Wan | Zendy; Qinghua Zhou | Zendy; ZhiGang Zhou | Zendy; Pei Wang | Zendy

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Dynamical Behaviors of the Stochastic Hopfield Neural Networks with Mixed Time Delays

Author(s) -

Li Wan,

Qinghua Zhou,

ZhiGang Zhou,

Pei Wang

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/384981

Subject(s) - mathematics , correctness , hopfield network , attractor , discrete time and continuous time , artificial neural network , linear matrix inequality , stability (learning theory) , control theory (sociology) , stochastic neural network , stochastic differential equation , exponential stability , mathematical optimization , computer science , recurrent neural network , mathematical analysis , nonlinear system , algorithm , statistics , physics , control (management) , quantum mechanics , machine learning , artificial intelligence

This paper investigates dynamical behaviors of the stochastic Hopfield neural networks with mixed time delays. The mixed time delays under consideration comprise both the discrete time-varying delays and the distributed time-delays. By employing the theory of stochastic functional differential equations and linear matrix inequality (LMI) approach, some novel criteria on asymptotic stability, ultimate boundedness, and weak attractor are derived. Finally, a numerical example is given to illustrate the correctness and effectiveness of our theoretical results

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