Consecutive identification of ANFIS-Based fuzzy systems with the aid of genetic data granulation

Authors:
Sung-Kwun Oh;Keon-Jun Park;Witold Pedrycz
Affiliations:
Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

We introduce a consecutive identification of ANFIS-based fuzzy systems with the aid of genetic data granulation to carry out the model identification of complex and nonlinear systems. The proposed model implements system structure and parameter identification with the aid of information granulation and genetic algorithms. The design methodology emerges as a hybrid structural optimization and parametric optimization. Information granulation realized with HCM clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise and the initial values of polynomial functions in the consequence. And the structure and the parameters of fuzzy model are identified by GAs and the membership parameters are tuned by GAs. In this case we exploit a consecutive identification. The numerical example is included to evaluate the performance of the proposed model.