Comparison principle and stability of stochastic delayed neural networks with Markovian switching

Abstract

This paper deals with the stability issue for a class of stochastic delayed neural networks with Markovian switching. The jumping parameters are determined by a continuous-time, discrete-state Markov chain. Different from the usual Lyapunov–Krasovskii functional and linear matrix inequality method, we first introduce and study a new comparison principle in the field of stochastic delayed neural networks. Then, we apply this new comparison principle to obtain several novel stability criteria of the suggested system. Moreover, an example is given to illustrate the theoretical results well.