Neuro-fuzzy system modeling based on automatic fuzzy clustering

作     者：Yuangang TANG Fuchun SUN Zengqi SUN 

作者机构：Department of Computer Science and Technology Tsinghua University Beijing 100084 China 

出 版 物：《控制理论与应用（英文版）》 (JOURNAL OF CONTROL THEORY AND APPLICATIONS)

年 卷 期：2005年第3卷第2期

页      面：121-130页

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：Neuro-fuzzy system Automatic fuzzy C-means Gradient descent Back propagation Recursive least square estimation Two-link manipulator 

摘      要：A neuro-fuzzy system model based on automatic fuzzy dustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts：1） Automatic fuzzy C-means （AFCM）, which is applied to generate fuzzy rttles automatically, and then fix on the size of the neuro-fuzzy network, by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2） *** least square estimation （RLSE）. It is used to update the parameters of Takagi-Sugeno model, which is employed to describe the behavior of the system;3） Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.