Dynamical Behaviors of Stochastic Hopfield Neural Networks with Both Time-Varying and Continuously Distributed Delays | Zendy

Qinghua Zhou | Zendy; Penglin Zhang | Zendy; Li Wan | Zendy

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Dynamical Behaviors of Stochastic Hopfield Neural Networks with Both Time-Varying and Continuously Distributed Delays

Author(s) -

Qinghua Zhou,

Penglin Zhang,

Li Wan

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 - 10.1155/2013/631734

Subject(s) - hopfield network , correctness , mathematics , attractor , artificial neural network , stability (learning theory) , exponential stability , control theory (sociology) , matrix (chemical analysis) , linear matrix inequality , mathematical optimization , computer science , mathematical analysis , algorithm , nonlinear system , artificial intelligence , control (management) , machine learning , physics , quantum mechanics , materials science , composite material

This paper investigates dynamical behaviors of stochastic Hopfield neural networks with both time-varying and continuously distributed delays. By employing the Lyapunov functional theory and linear matrix inequality, some novel criteria on asymptotic stability, ultimate boundedness, and weak attractor are derived. Finally, an example is given to illustrate the correctness and effectiveness of our theoretical results

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