Design of T-S fuzzy classifier via linear matrix inequality approach

Moon Hwan Kim, Jin Bae Park, Young Hoon Joo, Ho Jae Lee

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

Abstract

A linear matrix inequality approach to designing accurate classifier with a compact T-S(Takagi-Sugeno) fuzzy-rule is proposed, in which all the elements of the T-S fuzzy classifier design problem have been moved in parameters of a LMI optimization problem. Two-step procedure is used to effectively design the T-S fuzzy classifier with many tuning parameters: antecedent part and consequent part design. Then two LMI optimization problems are formulated in both parts and solved efficiently by using interior-point method. Iris data is used to evaluate the performance of the proposed approach. From the simulation results, the proposed approach showed superior performance over other approaches.

Original languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
PublisherSpringer Verlag
Pages406-415
Number of pages10
ISBN (Print)9783540283126
Publication statusPublished - 2006 Jan 1
Event2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China
Duration: 2005 Aug 272005 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3613 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005
CountryChina
CityChangsa
Period05/8/2705/8/29

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, M. H., Park, J. B., Joo, Y. H., & Lee, H. J. (2006). Design of T-S fuzzy classifier via linear matrix inequality approach. In Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings (pp. 406-415). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3613 LNAI). Springer Verlag.