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

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

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

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

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
Publisher	Springer Verlag
Pages	406-415
Number of pages	10
ISBN (Print)	9783540283126
Publication status	Published - 2006 Jan 1
Event	2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China Duration: 2005 Aug 27 → 2005 Aug 29

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3613 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005
Country	China
City	Changsa
Period	05/8/27 → 05/8/29

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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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.

Kim, Moon Hwan ; Park, Jin Bae ; Joo, Young Hoon ; Lee, Ho Jae. / Design of T-S fuzzy classifier via linear matrix inequality approach. Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Springer Verlag, 2006. pp. 406-415 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Kim, MH, Park, JB, Joo, YH & Lee, HJ 2006, Design of T-S fuzzy classifier via linear matrix inequality approach. in Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3613 LNAI, Springer Verlag, pp. 406-415, 2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005, Changsa, China, 05/8/27.

Design of T-S fuzzy classifier via linear matrix inequality approach. / Kim, Moon Hwan; Park, Jin Bae; Joo, Young Hoon; Lee, Ho Jae.

Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Springer Verlag, 2006. p. 406-415 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3613 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Kim MH, Park JB, Joo YH, Lee HJ. Design of T-S fuzzy classifier via linear matrix inequality approach. In Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Springer Verlag. 2006. p. 406-415. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).