Abstract
In this paper, according to classic M -matrix method, integral–differential inequality technique and Ito formula, we study asymptotic behavior in mean square sense of stochastic neural networks with infinitely distributed delays by establishing a generalized Halanay inequality. This is a new means for investigating asymptotic behavior of stochastic differential equation. Some useful results are derived. Especially, our methods can be extended to research p -moment asymptotic behavior easily. At last, example and simulations demonstrate the power of our methods.
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