Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach

K. W. Wong, L. T. Kóczy, T. D. Gedeon, A. Chong, D. Tikk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Fuzzy modeling has become very popular because of its main feature being the ability to assign meaningful linguistic labels to the fuzzy sets in the rule base. This paper examines Sugeno and Yasukawa's qualitative modeling approach, and addresses one of the remarks in the original paper. We propose a cluster search algorithm that can be used to provide a better projection of the output space to the input space. This algorithm can efficiently identify two or more fuzzy clusters in the input space that have the same output fuzzy cluster.

Original languageEnglish
Title of host publicationComputational Intelligence
Subtitle of host publicationTheory and Applications - International Conference, 7th Fuzzy Days, Proceedings
Pages536-549
Number of pages14
Publication statusPublished - Dec 1 2001
Event7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001 - Dortmund, Germany
Duration: Oct 1 2001Oct 3 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2206 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001
CountryGermany
CityDortmund
Period10/1/0110/3/01

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wong, K. W., Kóczy, L. T., Gedeon, T. D., Chong, A., & Tikk, D. (2001). Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach. In Computational Intelligence: Theory and Applications - International Conference, 7th Fuzzy Days, Proceedings (pp. 536-549). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2206 LNCS).