The Researches On Synchronization Analysis Of Semi-Markov Neural Networks

This article focuses on synchronization analysis of two classes of Neural Networks.One is single controller for synchronization of coupled neural networks with distributed timevarying delays,the other is the researches on synchronization analysis of semi-Markov neural networks.By constructing two different Lyapunov-Krasovskii functionals,using Chen’s integral inequality,Wirtinger-based integral inequality,the reciprocally convex approach,we obtained the sufficient conditions for synchronization.Chapter 1 mainly introduces the background of neural network synchronization analysis and the research status of coupled neural networks with time-varying delays,semi-Markov jump neural networks and an overview of the main content of this article.Chapter 2 is concerned with the synchronization analysis of coupled neural networks with distributed time-varying delays.only one controller is used for the synchronization of coupled neural networks.By applying a simple variation of the reciprocal convex and using Lyapunov functionals and linear matrix inequalities to derive some Tighter upper bounds of convex combination.By appropriately combining the reciprocal convex combination technique with Chen’s integral inequality,a less conservative synchronization criterion is obtained.Chapter 3 discusses the synchronization problem of a class of semi-Markov jump neural networks.A new model is constructed.Based on the Lyapunov-Krasovskii functional method,the synchronization is theoretically proved by Wirtinger-based integral inequality,Jensen inequality and Finsler’s lemma.