Identification of T-S fuzzy classifier via linear matrix inequalities

Moon Hwan Kim; Jin Bae Park; Weon Goo Kim; Young Hoon Joo

doi:10.1007/11589990_155

Identification of T-S fuzzy classifier via linear matrix inequalities

Moon Hwan Kim, Jin Bae Park, Weon Goo Kim, Young Hoon Joo

Department of Electrical and Electronic Engineering

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

1 Citation (Scopus)

Abstract

In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method.

Original language	English
Title of host publication	AI 2005
Subtitle of host publication	Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings
Publisher	Springer Verlag
Pages	1134-1137
Number of pages	4
ISBN (Print)	3540304622, 9783540304623
DOIs	https://doi.org/10.1007/11589990_155
Publication status	Published - 2005
Event	18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence - Sydney, Australia Duration: 2005 Dec 5 → 2005 Dec 9

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3809 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
Country/Territory	Australia
City	Sydney
Period	05/12/5 → 05/12/9

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/11589990_155

Cite this

Kim, M. H., Park, J. B., Kim, W. G., & Joo, Y. H. (2005). Identification of T-S fuzzy classifier via linear matrix inequalities. In AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings (pp. 1134-1137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3809 LNAI). Springer Verlag. https://doi.org/10.1007/11589990_155

Kim, Moon Hwan ; Park, Jin Bae ; Kim, Weon Goo ; Joo, Young Hoon. / Identification of T-S fuzzy classifier via linear matrix inequalities. AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Springer Verlag, 2005. pp. 1134-1137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Identification of T-S fuzzy classifier via linear matrix inequalities",

abstract = "In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method.",

author = "Kim, {Moon Hwan} and Park, {Jin Bae} and Kim, {Weon Goo} and Joo, {Young Hoon}",

year = "2005",

doi = "10.1007/11589990_155",

language = "English",

isbn = "3540304622",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1134--1137",

booktitle = "AI 2005",

address = "Germany",

note = "18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence ; Conference date: 05-12-2005 Through 09-12-2005",

}

Kim, MH, Park, JB, Kim, WG & Joo, YH 2005, Identification of T-S fuzzy classifier via linear matrix inequalities. in AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3809 LNAI, Springer Verlag, pp. 1134-1137, 18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence, Sydney, Australia, 05/12/5. https://doi.org/10.1007/11589990_155

Identification of T-S fuzzy classifier via linear matrix inequalities. / Kim, Moon Hwan; Park, Jin Bae; Kim, Weon Goo; Joo, Young Hoon.

AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Springer Verlag, 2005. p. 1134-1137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3809 LNAI).

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

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AU - Joo, Young Hoon

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N2 - In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method.

AB - In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method.

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

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Kim MH, Park JB, Kim WG, Joo YH. Identification of T-S fuzzy classifier via linear matrix inequalities. In AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2005. p. 1134-1137. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11589990_155

Identification of T-S fuzzy classifier via linear matrix inequalities

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