The Stability And Synchronization Analysis Of Neutral Neural Network Systems

In this paper,the mean-square exponential input-to-state stability of neutral neural networks and finite-time and fixed-time synchronization of fuzzy inertial cellular neural networks(FICNNs)are studied by means of stochastic analysis theory,finite-time stability theory,drive response control and integral inequality.In chapter 2,the mean-square exponential input-to-state stability problem of stochastic neutral Cohen-Grossberg neural networks with mixed delays is discussed.By constructing suitable Lyapunov-Krasovskii functional(LKF),and using Ito formula,Dynkin formula and stochastic analysis theory,some new sufficient conditions are obtained to ensure that the system achieves the mean-square exponential input-to-state stability.The correctness of the conclusion is verified by two numerical simulation examples.In chapter 3,finite-time and fixed-time synchronization problems of a class of fuzzy inertial cellular neural networks with mixed delays are studied.The initial system is converted into a first-order differential equation by means of appropriate variable substitution.Through finite-time stability theory,Lyapunov functional and two different controllers,the drive system and the response system can achieve finite-time and fixed-time synchronization respectively.Several numerical examples and numerical simulations are given to illustrate the validity of the theoretical results.