In this paper, the problem for recurrent neural networks is considered. It is stochastic and contains jumping parameters which are continuous-time Markov process. Delay is mode-dependent and this model is affected by multiplicative noise. Based on the Lyapunov stability theory combined with linear matrix inequalities (LMIs) techniques, we would get some new criteria to guarantee that they are robust stable and their L2 gains are less than γ ≫ 0. Introducing into some free weighting matrices would lead to much less conservative results. At last, one numerical example is given to illustrate the effectiveness of the proposed method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Robust stability criterion for stochastic recurrent neural networks with markovian jumping parameters, mode-dependent delays and multiplicative noise


    Beteiligte:
    Qiu, Ji-qing (Autor:in) / He, Hai-kuo (Autor:in) / Gao, Zhi-feng (Autor:in)


    Erscheinungsdatum :

    01.12.2008


    Format / Umfang :

    537748 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control

    Rakkiyappan, R. / Chandrasekar, A. / Lakshmanan, S. et al. | British Library Online Contents | 2015


    Robust stochastic stability and h∞ control for uncertain singular markovian jump systems with multiplicative noise

    Zhao, Yong / Zhang, Weihai / Xia, Jianwei et al. | British Library Online Contents | 2017



    Delay-dependent stability analysis for Markovian jump systems with interval time-varying-delays

    Zhao, X. D. / Zeng, Q. S. | British Library Online Contents | 2010


    Delay‐dependent stability criterion for discrete‐time systems with time‐varying delays

    Hua, Changchun / Wu, Shuangshuang / Bai, Zhenhua et al. | British Library Online Contents | 2017