Input‐to‐State Stability for Dynamical Neural Networks with Time‐Varying Delays | Zendy

Weisong Zhou | Zendy; Zhichun Yang | Zendy

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Input‐to‐State Stability for Dynamical Neural Networks with Time‐Varying Delays

Author(s) -

Weisong Zhou,

Zhichun Yang

Publication year - 2012

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 - 10.1155/2012/372324

Subject(s) - mathematics , control theory (sociology) , artificial neural network , nonlinear system , stability (learning theory) , state (computer science) , property (philosophy) , class (philosophy) , matrix (chemical analysis) , computer science , control (management) , algorithm , artificial intelligence , philosophy , physics , materials science , epistemology , quantum mechanics , machine learning , composite material

A class of dynamical neural network models with time-varying delays is considered. By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained. Finally, numerical examples are provided to illustrate the efficiency of the derived results

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