The paper deals with the globally asymptotically stability of dynamical neural networks with time-varying delays. The sufficient conditions for the globally asymptotically stable of the neural networks are obtained by Lyapunov-Razumikhin technique. Particularly, we discuss the stability conditions which do not require the activation functions to be differential, bounded, or monotone nondecreasing. Two examples are also applied to illustrate the efficiency of the results.


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    Title :

    Global asymptotic stability for a class of neural networks with time-varying delays


    Contributors:
    Guo, Yingxin (author) / Xu, Chao (author)


    Publication date :

    2014-08-01


    Size :

    100789 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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