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Asymptotic Stability of Stochastic Delayed Recurrent Neural Networks with Impulsive Effects

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Abstract

In this paper, the asymptotic stability for a class of stochastic neural networks with time-varying delays and impulsive effects are considered. By employing the Lyapunov functional method, combined with linear matrix inequality optimization approach, a new set of sufficient conditions are derived for the asymptotic stability of stochastic delayed recurrent neural networks with impulses. A numerical example is given to show that the proposed result significantly improve the allowable upper bounds of delays over some existing results in the literature.

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Authors and Affiliations

  1. Department of Mathematics, Sungkyunkwan University, Suwon, 440-746, South Korea

    R. Sakthivel

  2. Department of Mathematics, Periyar University, Salem, 636-011, India

    R. Samidurai

  3. Department of Mathematics, Anna University Coimbatore, Coimbatore, 641 047, India

    S. M. Anthoni

Authors
  1. R. Sakthivel
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  2. R. Samidurai
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  3. S. M. Anthoni
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Corresponding author

Correspondence to R. Sakthivel.

Additional information

Communicated by Qianchuan Zhao.

The work of R. Sakthivel was supported by the Korean Research Foundation Grant funded by the Korean Government with grant number KRF 2010-0003495.

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Sakthivel, R., Samidurai, R. & Anthoni, S.M. Asymptotic Stability of Stochastic Delayed Recurrent Neural Networks with Impulsive Effects. J Optim Theory Appl 147, 583–596 (2010). https://doi.org/10.1007/s10957-010-9728-8

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