An asymptotical stability criterion for discrete-time stochastic neural networks with Markovian jumping and time-varying mixed delays

Abstract

The global asymptotical stability problem is considered for a class of discrete-time stochastic recurrent neural networks(NNs) with Markovian jumping parameters and time-varying mixed delays in this paper. The mixed time delays include discrete delays and distributed delays, and both are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. The neural networks have a finite number of modes, and the modes may jump from one to another according to a discrete-time Markov chain. Based on the Lyapunov method and stochastic analysis approach, delay-interval dependent stability criterion is obtained in terms of linear matrix inequality(LMI) and generalizes existing results. Finally, a numerical example is given to demonstrate the effectiveness of the proposed results.