Robust Stability of Discrete-Time Stochastic Neural Networks with Markovian Jumping Parameters and Mode-Dependent Delays

This paper investigates the problem of robust stability for a class of uncertain discrete-time stochastic recurrent neural networks (RNNs) with Markovian jumping parameters and mode-dependent delays. Stochastic item is nonlinear, and jumping parameters are considered as discrete time, discrete-state Markov process. We can get novel robust stability conditions in terms of linear matrix inequality (LMI) approach. Furthermore, we will introduce into some free weighting matrices by adding to a zero item in order to lead to much less conservative results. At last, a numerical example is given to illustrate the effectiveness of the proposed method.