Synchronization Analysis Of Stochastic Fuzzy Cellular Neural Networks

Stochastic fuzzy cellular neural networks are a hot research topic in the field of nonlinear systems.Because of the huge potential of fuzzy cellular neural networks in applications,many scholars have devoted themselves to the theoretical research of fuzzy cellular neural networks and have achieved good results.The application of fuzzy cellular neural networks mainly depends on its dynamic behavior,so it is of great significance to analyze their dynamic behavior.Due to the huge potential of fuzzy cellular neural networks in application,many scholars have devoted themselves to the theoretical research of fuzzy cellular neural networks and have achieved good results.At present,the research on the dynamic behavior of fuzzy cellular neural networks mainly includes stability,synchronization,and convergence,etc.Synchronization means that multiple dynamic systems eventually tend to a common state through interaction.This article mainly discusses the synchronization of fuzzy cellular neural networks.In addition,the phenomenon of time delay is widespread in neural network systems,which is caused by the inherent characteristics of the system;at the same time,considering that the system in reality will be affected by modeling errors,random noise and external disturbances,such kinds of influence are inevitable.Although time delays and uncertainty may cause the entire system to oscillate or instability and make it more difficult for us to analyze the system,the system that contains both time delays and random disturbance is closer to the neural networks in reality,they have richer dynamic behavior and more practical research value,so it is necessary to consider adding random disturbance and time delays in the system.Based on the existing research,this paper mainly analyzes the synchronization of fuzzy cellular neural networks,and has obtained some good results.The main contents are as follows:1?The problem of the p th moment exponential synchronization of stochastic fuzzy cellular neural networks with time delays is discussed.Firstly,the error system is obtained by using the drive-response system,and some effective criteria of the p th moment exponent synchronization are obtained by using the state feedback control method,graph theory method and Lyapunov stability theory.Then,with the help of the obtained synchronization criterion and some inequality techniques,the global Lyapunov function of the error system is constructed,and the pth moment exponential synchronization of stochastic fuzzy cellular neural networks with time delay is studied.Finally,a numerical example is given to illustrate the validity of the theoretical results.2?The problem of the inverse optimal synchronization control for a class of stochastic fuzzy cellular neural networks with proportional time delays is discussed.Firstly,the error system is obtained by using the drive-response system,and with the help of Lyapunov stability theory and some inequality techniques,a new Lyapunov function is constructed and gives the inverse optimal control law to obtain the synchronization of the drive-response system.Then an inverse optimal control method is proposed,which does not need to solve the Hamilton-Jacobi-Issacs(HJI)equation.By using the graph theory and HJI equation,the optimal stability control of the minimum cost function is obtained,obtains the feedback control law and the optimal value function,realize the synchronization of the drive-response system under the influence of noise signals.Finally,a simulation example is given to verify the effectiveness of the proposed method.