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http://dx.doi.org/10.5370/KIEE.2011.60.1.184

Identification Methodology of FCM-based Fuzzy Model Using Particle Swarm Optimization  

Oh, Sung-Kwun (수원대 공대 전기공학과)
Kim, Wook-Dong (수원대 전기공학과)
Park, Ho-Sung (수원대 공대 기술연구소)
Son, Myung-Hee (한국전자통신연구원)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.60, no.1, 2011 , pp. 184-192 More about this Journal
Abstract
In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.
Keywords
Fuzzy C-Means Clustering(FCM); Particle swarm optimization; Weighted Least Square Etimator(WLSE); Fuzzy inference system;
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1 R. Quinlan, "Combining Instance-Based and Model-Based Learning," In Proceedings on the Tenth International Conference of Machine Learning, pp. 236-243, 1993.
2 W. Pedrycz and K. C. Kwak, "Linguistic Models as A Framework of User-Centric System Modelling," IEEE Trans. syst. Man Cybern. A, Vol. 36, No. 4, pp. 727-745, 2006.   DOI   ScienceOn
3 W. Pedrycz and K. C. Kwak, "The Development of Incremental Models," IEEE Trans. on Fuzzy Systems, Vol. 15, No. 3, pp. 507-518, 2007.   DOI   ScienceOn
4 http://archive.ics.uci.edu/ml
5 J. Haddadnia, K. Faez, and M. Ahmadi, "A Fuzzy Hybrid Learning Algorithm for Radial Basis Function Neural Network with Application in Human Face Recognition," Pattern Recognition, Vol. 36, No. 5, pp. 1187-1202, 2003.   DOI   ScienceOn
6 J. C. Bezdek, R. Ehrlich, and W. Full, "FCM: The Fuzzy C-Means Clustering Algorithm," Computers & Geoscience, Vol. 10, No. 2-3, pp. 191-203, 1984.   DOI   ScienceOn
7 L. Sanchez, I. Couso, and J. Casillas, "Genetic Learning of Fuzzy Rules Based on Low Quality Data," Fuzzy Sets and Systems, Vol. 160, No. 17, pp. 2524-2552, 2009.   DOI   ScienceOn
8 J. Gollub and G. Sherlock, "Clustering Microarray Data," Methods in Enzymology, Vol. 411, No. 194-213, 2006.
9 A. Hajdu and T. Toth, "Approximating Non-Metrical Minkowski Distances in 2D," Pattern Recognition Letter, Vol. 29, No. 6, pp. 813-821, 2008.   DOI   ScienceOn
10 Z-B. Xu, H. Q, J. Peng, and B. Zhang, "A Comparative Study of Two Modeling Approaches in Neural Network," Neural Networks, Vol. 17, No. 1, pp. 73-85, 2004.   DOI   ScienceOn
11 J. N. Choi, Y. I. Lee, and S. K. Oh, "Fuzzy Radial Basis Function Neural Networks with Information Granulation and Its Genetic Optimization," Lecture Notes in Computer Science, Vol. 5552, pp. 127-134, 2009.
12 F. Tamaki, A. Kanagawa and H. Ohta, "Identification of Membership Functions based on Fuzzy Observation Data," Fuzzy Sets and System, Vol. 93, No. 3, pp. 311-318, 1998.   DOI   ScienceOn
13 S. G. Cao and N. W. Rees, "Identification of Dynamic Fuzzy Models," Fuzzy Sets and Systems, Vol. 74, No. 3, pp. 307-320, 1995.   DOI   ScienceOn
14 S. K. Oh, W. Pedrycz, and K. J. Park, "Structural Developments of Fuzzy Systems with The Aid of Information Granulation," Simulation Modelling Practice and Theory, Vol. 15, No. 10, pp. 1292-1309, 2007.   DOI   ScienceOn
15 J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms," Plenum, New York, 1981.
16 J. Kennedy and R. Everhart, "Particle Swarm Optimization," Proc. of IEEE International Conference on Neural Networks," Vol. 4, pp. 1942-1948, 1995.
17 A. Kandal, L. Li, and Z. Cao, "Fuzzy Inference and Its Application to Control Systems," Fuzzy Sets and Sytems, Vol. 48, No. 1, pp. 99-111, 1992.   DOI   ScienceOn
18 L. Zhao, Y. Yang, and Y. Zeng, "Eliciting Compact T-S Fuzzy Models Using Subtractive Clustering and Coevolutaionary Particle Swarm Optimization," Neurocomputing, Vol. 72, No. 10-12, pp. 2569-2575, 2009.   DOI   ScienceOn