Design of t–s fuzzy classifier via linear matrix inequality approach

Authors:
Moon Hwan Kim;Jin Bae Park;Young Hoon Joo;Ho Jae Lee
Affiliations:
Department of Electrical and Electronic Engineering, Yonsei University, Seodaemun-gu, Seoul, Korea;Department of Electrical and Electronic Engineering, Yonsei University, Seodaemun-gu, Seoul, Korea;School of Electronic and Information Engineering, Kunsan National University, Kunsan, Chonbuk, Korea;Department of Electrical and Electronic Engineering, Yonsei University, Seodaemun-gu, Seoul, Korea
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

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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.