Optimization of fuzzy systems based on fuzzy set using genetic optimization and information granulation

Authors:
Sung-Kwun Oh;Keon-Jun Park;Witold Pedrycz
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
Venue:
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Year:
2005

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Abstract

In this study, we propose a fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Information granules are sought as associated collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Information granulation realized with Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is evaluated with using two numerical examples and is contrasted with the performance of conventional fuzzy models in the literature.