Title :
Constructing a kind of fuzzy systems based on neural networks techniques
Author :
Qing, Ming ; Zhao, Hai-Liang ; Xia, Shi-Fen ; Wang, Xue-Fang
Author_Institution :
Dept. of Math., Southwest Jiaotong Univ., Sichuan, China
Abstract :
In this paper, a method to model a kind of nonlinear fuzzy system with fuzzy border is presented. BP networks (BPN) possess efficient capability of approximating nonlinear function and radius base function networks (RBFN) have the fast training speed. These advantages of BPN and RBFN can be combined with clustering techniques to improve system modeling. Firstly, the system structure is obtained by clustering. Secondly the BPN is employed to generate rule base´s antecedent function and RBFN to approximate each rule´s conclusion function, respectively. So the initial construction of the system can be acquired. Thirdly, structure design and training of networks are discussed in detail. Finally, the structure optimization and overstudy of RBFN are discussed.
Keywords :
backpropagation; function approximation; fuzzy systems; nonlinear systems; optimisation; radial basis function networks; BP networks; BPN; RBFN; clustering techniques; function approximation; fuzzy border; networks structure design; networks training; neural networks techniques; nonlinear fuzzy system; radius base function networks; rule bases antecedent function; structure optimization; system modeling; system structure; Control system synthesis; Electronic mail; Function approximation; Fuzzy control; Fuzzy systems; Mathematical model; Mathematics; Modeling; Neural networks; Radial basis function networks;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1259974
