Global asymptotic stability of stochastic BAM neural networks with distributed delays and reaction–diffusion terms
Copyright policy )Global asymptotic stability of stochastic BAM neural networks with distributed delays and reaction–diffusion terms
AbstractThis paper is concerned with global asymptotic stability of a class of reaction–diffusion stochastic Bi-directional Associative Memory (BAM) neural networks with discrete and distributed delays. Based on suitable assumptions, we apply the linear matrix inequality (LMI) method to propose some new sufficient stability conditions for reaction–diffusion stochastic BAM neural networks with discrete and distributed delays. The obtained results are easy to check and improve upon the existing stability results. An example is also given to demonstrate the effectiveness of the obtained results.
Mixed delays, Global asymptotic stability, Computational Mathematics, Reaction–diffusion, Applied Mathematics, Stochastic BAM neural networks
Mixed delays, Global asymptotic stability, Computational Mathematics, Reaction–diffusion, Applied Mathematics, Stochastic BAM neural networks
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