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

Jung Hsien Chiang, Pei Yi Hao

Research output: Contribution to journalArticle

175 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Volume12
Issue number1
DOIs
Publication statusPublished - 2004 Feb 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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