Fuzzy rule-based support vector regression system
Ling WANG
Zhichun MU
Hui GUO
摘要：In this paper,we design a fuzzy rule-based support vector regression system.The proposed system utilizes the advantages of fuzzy model and support vector regression to extract support vectors to generate fuzzy if-then rules from the training data set.Based on the first-order linear Tagaki-Sugeno (TS) model,the structure of rules is identified by the support vector regression and then the consequent parameters of rules are tuned by the global least squares method.Our model is applied to the real world regression task.The simulation results gives promising performances in terms of a set of fuzzy rules,which can be easily interpreted by humans.
机标关键词：support vector regressionleast squares methodsimulation resultsfuzzy modeldata set
分类号：TP2(自动化技术及设备)
资助基金：国家科技攻关项目(2002 AA412010)科技部科研项目(2003EG113016)
论文发表日期：2005-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：5( 230-234 )
英文信息