Browse > Article

Genetic Optimization of Fuzzy C-Means Clustering-Based Fuzzy Neural Networks  

Choi, Jeoung-Nae (수원대학 전기공학과)
Kim, Hyun-Ki (수원대학 전기공학과)
Oh, Sung-Kwun (수원대학 전기공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.57, no.3, 2008 , pp. 466-472 More about this Journal
Abstract
The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.
Keywords
Fuzzy Neural Networks; Fuzzy C-Means clustering; Hierachical Fair Competition based Parallel Genetic Algorithm; Least square estimator;
Citations & Related Records

Times Cited By SCOPUS : 1
연도 인용수 순위
1 W. Pderyca and G. Vukovich, "Granular neural networks," Neurocomputing, Vol. 36, pp. 205-224, 2001.   DOI   ScienceOn
2 Hu, J.J., Goodman, E.: The Hierarchical Fair Competition (HFC) Model for Parallel Evolutionary Algorithms. Proceedings of the 2002 Congress on Evolutionary Computation: CEC2002. IEEE. Honolulu. Hawaii. (2002)
3 L. X. Wang, J. M. Mendel, "Generating fuzzy rules from numerical data with applications," IEEE Trans. Systems, Man, Cybern., Vol. 22, No. 6, pp. 1414-1427, 1992.   DOI   ScienceOn
4 A. Staiano. J. Tagliaferri, W. Pedrycz, "Improving RBF networks performance in regression tasks by means of a supervised fuzzy clusering"Automatic structure and parameter," Neurocomputing, Vol. 69, pp. 1570-1581, 2006   DOI   ScienceOn
5 George E. Tsekouras, "On the use of the weighted fuzzy c-means in fuzzy modeling," Advances in Engineering Software, Vol. 36, pp. 287-300, 2005   DOI   ScienceOn
6 S.K. Oh, W. Pedrycz, "A new approach to self-organizing multi-layer fuzzy polynomial neural networks based on genetic optimization," Advanced Engineering Informatics, Vol. 18, pp. 29-39, 2004   DOI   ScienceOn
7 L. P. Maguire, B. Roche, T. M. McGinnity, L. J. McDaid, "Predicting a chaotic time series using a fuzzy neural network," Information Sciences, Vol. 112, pp. 125-136, 1998.   DOI   ScienceOn
8 L.J. Herrera, H.Pomares, I.Rojas, "Tase, a Taylor series-based fuzzy system model that combines interpertability and accuracy," Fuzzy Set and systems. Vol. 153, pp. 403-427, 2005   DOI   ScienceOn
9 Lin, S.C., Goodman, E., Punch, W.: Coarse-Grain Parallel Genetic Algorithms: Categorization and New Approach. IEEE Conf. on Parallel and Distrib. Processing. Nov. 1994
10 S.-K. Oh and W. Pedrycz, "Identification of Fuzzy Systems by means of an Auto- Tuning Algorithm and Its Application to Nonlinear Systems," Fuzzy Sets and Syst., Vol. 115, No. 2, pp. 205-230, 2000.   DOI   ScienceOn
11 J.C. Bezdek, J. Keller, R.Krisnapuram, N.R. Pal, "Fuzzy Models and Algorithms for Pattern Recognition and Image Processing," Kluwer Academic Publisher, Dordrecht, 1999
12 P. R. Krishnaiah and L. N. Kanal, editors. Classification, pattern recognition, and reduction of dimensionality, Vol. 2 of Handbook of Statistics. North-Holland, Amsterdam, 1982.
13 J.C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms," Plenum, New York, 1981
14 T. Tagaki and M. sugeno, "Fuzzy identification of system and its applications to modeling and control", IEEE Trans. Syst. Cybern., Vol. SMC-15, No. 1, pp. 116-132, 1985   DOI
15 F. Behloul, R.P.F. Lelieveldt, A. Boudraa, J.H.C. Reiber, "Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data", Pattern Recognition Vol. 35, pp. 659-675, 2002   DOI   ScienceOn
16 J. S. R. Jang,　"ANFIS: Adaptive-Network-Based Fuzzy Inference System," IEEE Trans. System, Man, and Cybern., Vol. 23, No. 3, pp. 665-685, 1993.   DOI   ScienceOn