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: Contribution to journalConference article

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
Pages (from-to)406-415
Number of pages10
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3613
Issue numberPART I
Publication statusPublished - 2005 Oct 27
EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

Fingerprint

Fuzzy Classifier
Linear matrix inequalities
Matrix Inequality
Linear Inequalities
Classifiers
Optimization Problem
Iris
Interior Point Method
Parameter Tuning
Fuzzy rules
Fuzzy Rules
Tuning
Classifier
Evaluate
Design
Simulation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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

In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 3613, No. PART I, 27.10.2005, p. 406-415.

Research output: Contribution to journalConference article

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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

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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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