Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling: A New Approach

Jung Hsien Chiang, Pei Yi Hao

研究成果: Article同行評審

180 引文 斯高帕斯(Scopus)


This paper describes a fuzzy modeling framework based on support vector machine, a rule-based framework that explicitly characterizes the representation in fuzzy inference procedure. The support vector learning mechanism provides an architecture to extract support vectors for generating fuzzy IF-THEN rules from the training data set, and a method to describe the fuzzy system in terms of kernel functions. Thus, it has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions. The performance of the proposed approach is compared to other fuzzy rule-based modeling methods using four data sets.

頁(從 - 到)1-12
期刊IEEE Transactions on Fuzzy Systems
出版狀態Published - 2004 二月 1

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 計算機理論與數學
  • 人工智慧
  • 應用數學


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