Abstract
This paper is concerned with the mean-square stability of the Split-Step Backward Euler method for stochastic delayed Hopfield neural networks. The sufficient conditions to guarantee the mean-square stability of the Split-Step Backward Euler method are given. Moreover, an example of the comparison of our method with the Euler–Maruyama method is used to show the superiority of our method.
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