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http://dx.doi.org/10.5391/JKIIS.2004.14.1.046

TS Fuzzy Classifier Using A Linear Matrix Inequality  

Kim, Moon-Hwan (연세대학교 전기전자공학과)
Joo, Young-Hoon (군산대학교 전자정보공학부)
Park, Jin-Bae (연세대학교 전기전자공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.1, 2004 , pp. 46-51 More about this Journal
Abstract
his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.
Keywords
Wisconsin breast cancer diagnostic database;
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