Mean-square stability analysis of discrete-time stochastic Markov jump recurrent neural networks with mixed delays

In this paper, the problem of mean-square stability analysis for discrete-time stochastic Markov jump recurrent neural networks with time-varying mixed delays is considered. The Markov jumping transition probabilities are assumed completely unknown but piecewise homogeneous, and the mixed time delays under consideration comprise both time-varying discrete delay and infinite distributed delay. In the framework of the delay partitioning approach, the informations of the delay distribution probability are fully considered. With a novel Lyapunov functional, a sufficient delay-dependent condition is established, which is characterized in terms of Linear Matrix Inequalities (LMIs). Finally, a numerical example is given to demonstrate the effectiveness of the obtained results.