Title :
Fuzzy rule modeling based on FCM and support vector regression
Author :
Wang, Ling ; Mu, Zhi-Chun ; Fu, Dong-mei
Author_Institution :
Inf. Eng. Sch., Univ. of Sci. & Technol., Beijing
Abstract :
To design a fuzzy rule-based modeling framework with good generalization ability has been an active research topic for a long time. As a powerful machine learning approach for function approximation and regression estimation problems, support vector regression (SVR) is known to have good generalization ability. In this paper, we adopt the FCM clustering algorithm to group data patterns into clusters, after FCM clustering, the membership grade are applied to generate fuzzy kernel. Then, the support vector learning with fuzzy kernel provides a fuzzy IF-THEN rules architecture. In terms of fuzzy rules, the overall fuzzy inference system can be calculated by weighting the inferred output values from each cluster with their corresponding membership values. Experimental results show that the proposed method can achieve good approximation performance.
Keywords :
function approximation; fuzzy reasoning; fuzzy set theory; generalisation (artificial intelligence); pattern clustering; regression analysis; support vector machines; FCM clustering algorithm; data patterns; function approximation; fuzzy IF-THEN rules architecture; fuzzy inference system; fuzzy kernel; fuzzy rule-based modeling; machine learning; membership grade; regression estimation; support vector learning; support vector regression; Approximation algorithms; Clustering algorithms; Function approximation; Fuzzy sets; Fuzzy systems; Kernel; Machine learning; Machine learning algorithms; Partitioning algorithms; Support vector machines; FCM; Fuzzy Inference System; Fuzzy Kernel; Support Vector Regression;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620695