Stability analysis for stochastic discrete-time recurrent neural networks with time-varying delay components

This paper is concerned with analysis problem for the stability of the a stochastic discrete-time neural networks (DNNs) with discrete time-varying delay. By used some novel analysis techniques, stability theory and Lyapunov -Krasovskii function, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally asymptotically stable in mean square. Decomposing the delay interval approach is also employed in this paper, and the Lyapunov -Krasovskii functionals (LKFs) are constructed on these intervals, such that a new stability criterion is proposed in terms of Linear Matrix Inequalities (LMIs). Numerical examples are given to demonstrate the effectiveness of the proposed method and the applicability of the proposed method.