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Delay-independent stability of stochastic reaction–diffusion neural networks with Dirichlet boundary conditions

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Abstract

This paper deals with the problem of global stability of stochastic reaction–diffusion recurrent neural networks with continuously distributed delays and Dirichlet boundary conditions. The influence of diffusion, noise and continuously distributed delays upon the stability of the concerned system is discussed. New stability conditions are presented by using of Lyapunov method, inequality techniques and stochastic analysis. Under these sufficient conditions, globally exponential stability in the mean square holds, regardless of system delays. The proposed results extend those in the earlier literature and are easier to verify.

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Acknowledgements

The authors are grateful to the referees for their careful reading and very constructive comments on the original manuscript.

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Authors and Affiliations

  1. School of Mathematical Sciences and Computing Technology, Central South University, 410075, Changsha, Hunan, China

    Zaiming Liu & Jun Peng

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  1. Zaiming Liu
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  2. Jun Peng
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Corresponding author

Correspondence to Jun Peng.

Additional information

Research supported by National Science Foundation of China (No. 10371133).

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Liu, Z., Peng, J. Delay-independent stability of stochastic reaction–diffusion neural networks with Dirichlet boundary conditions. Neural Comput & Applic 19, 151–158 (2010). https://doi.org/10.1007/s00521-009-0268-9

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  • DOI: https://doi.org/10.1007/s00521-009-0268-9

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