FUZZY RULE MODELING BASED ON FCM AND SUPPORT VECTOR REGRESSION

摘要：

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.

关键词： FCM Fuzzy Kernel Support Vector Regression Fuzzy Inference System

作者: LING WANG ZHI-CHUN MU DONG-MEI FU

作者单位: Information Engineering School, University of Science and Technology Beijing 100083

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1789-1794

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）