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

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  • Weisong Zhou
  • Zhichun Yang

Abstract

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.

Suggested Citation

  • Weisong Zhou & Zhichun Yang, 2012. "Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays," Abstract and Applied Analysis, Hindawi, vol. 2012, pages 1-12, December.
    • Handle: RePEc:hin:jnlaaa:372324
    DOI: 10.1155/2012/372324
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