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In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criterion is derived to ensure the global, asymptotic stability of the addressed system in the mean square. The criterion can be checked easily by the LMI Control Toolbox in...
This paper is concerned with the asymptotic stability analysis problem for stochastic neural networks with distributed and time-varying delays. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which take into account the ranges of delays, a new stability criterion is established in terms of linear matrix inequalities...
This paper is concerned with asymptotic stability of stochastic neural networks with time-varying delay. Distinct difference from other analytical approach lies in ldquolinearizationrdquo of neural network model, by which the considered neural network model is transformed into a linear time-variant system. A sufficient condition is derived such that for all admissible disturbance, the considered neural...
In this paper, the delay-dependent state estimation problem for a class of neural networks with time-varying discrete and distributed delays is concerned. The activation function is assumed to be neither monotonic nor differentiable, and a delay-dependent condition is established in terms of linear matrix inequality (LMI) to guarantee the dynamics of the estimation error is globally asymptotically...
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