Global asymptotic stability analysis for stochastic neutral-type delayed neural networks

Abstract

In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criterion is derived to ensure the global, asymptotic stability of the addressed system in the mean square. The criterion can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the feasibility and effectiveness of the results.