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This paper is concerned with analyzing mean square exponential stability of stochastic delayed neural networks subject to parametric uncertainties. The discretized Lyapunov functional technique is first utilized to construct a new Lyapunov functional in order to effectively deal with the time-varying delay. Then the free-weighting matrix technique and the convex combination method are used to establish...
This letter is concerned with the mean square exponential stability problem for a class of uncertain stochastic neural networks with time-varying delay. The activation functions are assumed to be neither monotonic, nor differentiable. The proposed delay-dependent stability criterion is derived by utilizing the constructed Lyapunov-Krasovskii functional and the novel technique. A numerical example...
The stability of stochastic delayed cellular neural networks (SDCNN) is investigated in this paper. With the help of Lyapunov function, a set of novel sufficient conditions on mean square exponential stability is given. A numerical example is also given to illustrate the effectiveness of our results.
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