Exponential Stability for Discrete-Time Stochastic BAM Neural Networks with Discrete and Distributed Delays | Zendy

R. Raja | Zendy; R. Sakthivel | Zendy; S. Marshal Anthoni | Zendy

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Exponential Stability for Discrete-Time Stochastic BAM Neural Networks with Discrete and Distributed Delays

Author(s) -

R. Raja,

R. Sakthivel,

S. Marshal Anthoni

Publication year - 2011

Publication title -

isrn discrete mathematics

Language(s) - English

Resource type - Journals

ISSN - 2090-7788

DOI - 10.5402/2011/153409

Subject(s) - exponential stability , discrete time and continuous time , control theory (sociology) , artificial neural network , matlab , stability (learning theory) , equilibrium point , stochastic neural network , computer science , toolbox , mathematics , mathematical optimization , recurrent neural network , control (management) , nonlinear system , artificial intelligence , mathematical analysis , machine learning , statistics , physics , quantum mechanics , programming language , differential equation , operating system

This paper deals with the stability analysis problem for a class of discrete-time stochasticBAM neural networks with discrete and distributed time-varying delays. By constructing a suitableLyapunov-Krasovskii functional and employing M-matrix theory, we find some sufficientconditions ensuring the global exponential stability of the equilibrium point for stochastic BAMneural networks with time-varying delays. The conditions obtained here are expressed in termsof LMIs whose feasibility can be easily checked by MATLAB LMI Control toolbox. A numericalexample is presented to show the effectiveness of the derived LMI-based stability conditions.

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