Analyzing the Stochastic Stability of Neural Networks with Continuous-Time Semi-Markov Jump Parameters

Abstract

This paper addresses the issue of stochastic stability for continuous-time semi-Markovian jump neural networks. Initially, according to the characteristics of the time-delay interval, the Lyapunov-Krasovskii functional (LKF) with the semi-Markov process is constructed to ensure the stability of the switching networks.his paper addresses the issue of stochastic stability for continuous-time semi-Markovian jump neural networks. Initially, according to the characteristics of the time-delay interval, the Lyapunov-Krasovskii functional (LKF) with the semi-Markov process is constructed to ensure the stability of the switching networks.T Next, the promoted double-integral inequality lemma is utilized to estimate the weak infinitesimal operator of the designed LKF, and this paper establishes a time-delay correlation criterion. Moreover, by the criterion is combined with the Lyapunov stability theory to make the system reach stability in the mean-square sense. Finally, the paper provides an example to demonstrate the effectiveness of the proposed approach.