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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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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DOI: https://doi.org/10.1007/s10957-010-9728-8