Application Of Intelligent Optimization Algorithm In Fuzzy Indentification

Fuzzy model belongs to nonlinear model,which has been proved to be a universal approximator.Therefore,fuzzy modeling is an effective method for the modeling and control of nonlinear systems and uncertain systems.The traditional methods of fuzzy identification are difficult to achieve satisfactory accuracy,because of its limitations.So,the reasonable parameters will be an effective way to improve the accuracy of identification.With the development and maturity of the intelligent optimization algorithm,it is applied to the optimization of fuzzy model more and more,and it provides an effective method to improve the precision and efficiency of model identification.In this paper,the application of intelligent optimization algorithm in fuzzy identification is studied.The specific research work is as follows:Firstly,the research background and significance of the subject are summarized,and the development and research status of fuzzy identification and intelligent optimization algorithm are summarized.This chapter introduces the basic knowledge of fuzzy model identification,which lays the theoretical foundation for the following chapters.Secondly,two new fuzzy identification methods are proposed based on fuzzy theory and intelligent optimization algorithm.The first method is fuzzy identification based on the T-S model and the improved cat swarm optimization(CSO)algorithm.The hybrid algorithm makes use of the advantages of the two algorithms,and improves the identification accuracy and convergence speed.The second method uses Fuzzy c-Means(FCM)algorithm to optimize the clustering center.Then,the global search ability of the improved CSO is used to optimize the clustering center.The algorithm solves the problem that the FCM is easy to fall into local optimum,and improves the precision and efficiency of the fuzzy identification.Then,aiming at the problem that particle swarm optimization algorithm(PSO)is easy to fall into the local extremum in the treatment of high dimensional complex functions,a PSO algorithm based on tent chaotic map is proposed,and the improved PSO is used to optimize the parameters of the fuzzy model.The simulation results are satisfactory.Finally,the generalized predictive control(GPC)algorithm of chaotic system based on fuzzy model is studied.The FCM is applied to the GPC algorithm of chaotic systems,and a more efficient GPC algorithm is proposed.The improved FCM algorithm is used to identify the Hénon chaotic system,and the local dynamic linear model is obtained.The GPC algorithm is used to control the system.Simulation results verify the effectiveness of the proposed method.