Identification of t–s fuzzy classifier via linear matrix inequalities

Authors:
Moon Hwan Kim;Jin Bae Park;Weon Goo Kim;Young Hoon Joo
Affiliations:
Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea;Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea;School of Electronic and Information Engineering, Kunsan National University, Kunsan, Chonbuk, Korea;School of Electronic and Information Engineering, Kunsan National University, Kunsan, Chonbuk, Korea
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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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.