Mean square exponential stability of stochastic recurrent neural networks with time-varying delays

The stability of a class of stochastic Recurrent Neural Networks with time-varying delays is investigated in this paper. With the help of the Lyapunov function and the Dini derivative of the expectation of V(t,X(t)) ''along'' the solution X(t) of the model, a set of novel sufficient conditions on mean square exponential stability has been established. An example is also given to illustrate the effectiveness of our results.

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