Less conservative stability criteria for stochastic discrete-time recurrent neural networks with the time-varying delay ?
This paper is concerned with the analysis problem for the stability of stochastic discrete-time recurrent neural networks (RNNs) with discrete time-varying delays. By using stability theory and LyapunovKrasovskii function based on delay partitioning, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally asymptotically stable in mean square. Numerical examples are given to demonstrate the effectiveness of the proposed method and the applicability of the proposed method.
lyapunovkrasovskii functional linear matrix inequality (lmi) discrete-time neural networks discrete time-delays stochastic disturbances