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

K. W. Wong, L. 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages536-549
Number of pages14
Volume2206 LNCS
Publication statusPublished - 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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Fuzzy sets
Modeling
Linguistics
Labels
Cluster Algorithm
Fuzzy Modeling
Rule Base
Output
Search Algorithm
Fuzzy Sets
Assign
Projection

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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

Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach. / Wong, K. W.; Kóczy, L.; Gedeon, T. D.; Chong, A.; Tikk, D.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2206 LNCS 2001. p. 536-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2206 LNCS).

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

Wong, KW, Kóczy, L, Gedeon, TD, Chong, A & Tikk, D 2001, Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2206 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2206 LNCS, pp. 536-549, 7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001, Dortmund, Germany, 10/1/01.
Wong KW, Kóczy L, Gedeon TD, Chong A, Tikk D. Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2206 LNCS. 2001. p. 536-549. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Wong, K. W. ; Kóczy, L. ; Gedeon, T. D. ; Chong, A. ; Tikk, D. / Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2206 LNCS 2001. pp. 536-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{58ec9557c1fa41ae95b956c591517b5f,
title = "Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach",
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.",
author = "Wong, {K. W.} and L. K{\'o}czy and Gedeon, {T. D.} and A. Chong and D. Tikk",
year = "2001",
language = "English",
isbn = "3540427325",
volume = "2206 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "536--549",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Improvement of the cluster searching algorithm in Sugeno and Yasukawa's qualitative modeling approach

AU - Wong, K. W.

AU - Kóczy, L.

AU - Gedeon, T. D.

AU - Chong, A.

AU - Tikk, D.

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=23044529011&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=23044529011&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540427325

SN - 9783540427322

VL - 2206 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 536

EP - 549

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -