Stochastic Robust Stability Analysis for Markovian Jump Neural Networks with Time Delay

Abstract

The problem of stochastic robust stability analysis for uncertain delayed neural networks with Markovian jumping parameters is investigated. Based on Lyapunov stability theory, a novel approach for stability analysis of neural networks is developed. The sufficient conditions of stochastic robust stability are given in terms of linear matrix inequalities (LMIs). The stable criteria represented in LMI setting are less conservative and more computationally efficient than existing results reported in other literature.