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

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

doi:10.1007/11539506_53

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: Contribution to journal › Conference article › peer-review

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
Pages (from-to)	406-415
Number of pages	10
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	3613
Issue number	PART I
DOIs	https://doi.org/10.1007/11539506_53
Publication status	Published - 2005
Event	Second International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/11539506_53

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title = "Design of T-S fuzzy classifier via linear matrix inequality approach",

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.",

author = "Kim, {Moon Hwan} and Park, {Jin Bae} and Joo, {Young Hoon} and Lee, {Ho Jae}",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Second International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 ; Conference date: 27-08-2005 Through 29-08-2005",

year = "2005",

doi = "10.1007/11539506_53",

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journal = "Lecture Notes in Computer Science",

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TY - JOUR

T1 - Design of T-S fuzzy classifier via linear matrix inequality approach

AU - Kim, Moon Hwan

AU - Park, Jin Bae

AU - Joo, Young Hoon

AU - Lee, Ho Jae

PY - 2005

Y1 - 2005

N2 - 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.

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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