Stochastic stability analysis for a neutral-type neural networks with Markovian jumping parameters

Abstract

In this paper, the stability problem is studied for a class of stochastic neutral-type neural networks with Markovian jumping parameters. By using fixed point theorem, the existence and uniqueness of solution for the neural networks system are obtained. Furthermore, based on the Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the mean square stability of the neural networks. An example is given to show the effectiveness of the proposed stability criterion.