Publication

Abstract : This paper investigates the robust stability analysis for a class of uncertain stochastic neural networks (SNNs)with markovian jump and time-varying delays. Based on the stochastic analysis approach & Lyapunov-Krasovskii functional, a delay probability distribution dependent sufficient condition is obtained in the linear matrix inequality (LMI) form such that delayed markovian jump SNNs are robustly globally asymptotically stable in the mean square for all admissible uncertainties. An important feature of the result is that the stability conditions are dependent on the probability distribution of delays and upper bound of the derivative is allowed to be greater than or equal to 1. Numerical examples are given for the comparison to illustrate the effectiveness of our results.