Browse > Article

Optimization of FCM-based Radial Basis Function Neural Network Using Particle Swarm Optimization  

Choi, Jeoung-Nae (대림대학교 전기과)
Kim, Hyun-Ki (수원대학교 전기공학과)
Oh, Sung-Kwun (수원대학교 전기공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.57, no.11, 2008 , pp. 2108-2116 More about this Journal
Abstract
The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). 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 - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN 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 PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.
Keywords
Radial Basis Function Neural Networks; Fuzzy C-Means clustering; Particle Swarm Optimization; Weighted Least Square Estimator;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By SCOPUS : 1
연도 인용수 순위
1 T. Tagaki and M. sugeno, 'Fuzzy identification of system and its applications to modeling and control', IEEE Trans. Syst. Cyber., Vol. SMC-15, No. 1, pp. 116-132, 1985   DOI
2 M. Sugeno, T. Yasukawa, Linguistic modeling based on numerical data, in: IFSA'91 Brussels, Computer, Management & System Science, pp. 264-67, 1991
3 G.E. Tsekouras, On the use of the weighted fuzzy C- Means in fuzzy modeling, Adv. Eng. Software, Vol. 36, pp. 287-300, 2005   DOI   ScienceOn
4 J. Kennedy and R. Eberhart, 'Particle Swarm Optimization', Proc. of IEEE International Conference on Neural Networks, Vol. 4, pp.1942-1948, Perth, Australia, 1995
5 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
6 W. Pedrycz, An identification algorithm in fuzzy relational system, Fuzzy Sets and Systems, Vol. 13, No. 2, pp. 153-167, 1984   DOI   ScienceOn
7 J.C. Bezdek, J. Keller, R.Krisnapuram, N.R. Pal, 'Fuzzy Models and Algorithms for Pater Recognition and Image Processing,' Kluwer Academic Publisher, Dordrecht, 1999
8 J. S. R. Jang, 'ATFIS: Adaptive-Network Based Fuzzy Inference System,' IEEE Trans. System, Man, and Cybern., Vol. 23, No. 3, pp. 665-685, 1993   DOI   ScienceOn
9 H.Pomares, I.gnacio, J. Ortega, J. Gonzalez, A. Prieto, 'A Systematic Approach to a Self-Generating Fuzzy Rule-Table for Function Approximation,' IEEE Transaction on systems, man, and cybernetics, Vol. 30, No. 3, pp. 431-447, 2000   DOI   ScienceOn
10 R.M. Tong, The evaluation of fuzzy models derived from experimental data, Fuzzy Sets and Systems, Vol. 4, No. 1, pp. 1-12, 1980   DOI   ScienceOn
11 W. Pderyca and G. Vukovich, 'Granular neural network,' Neurocomputing, Vol. 36, pp. 205-224, 2001   DOI   ScienceOn
12 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. 15, No. 2, pp. 205-230, 2000
13 J.N. Choi, S.K. Oh, H.K. Kim, 'Genetic Optimization of Fuzzy C-Means Clustering-based Fuzzy Neural Networks', Trans. KIEE. Vol. 57, No.3, Mar, pp. 466-472, 2008   과학기술학회마을
14 A. Staiano. J. Tagliaferri, W. Pedrycz, 'Improving RBF net works performance in regression tasks by means of a supervised fuzzy clusering' Automatic structure and parameter,' Neurocomputing, Vol. 69, pp. 1570-1581, 2006   DOI   ScienceOn
15 C.W. Xu, Y Zailu, Fuzzy model identification self-learning for dynamic system, IEEE Trans System Man Cybernet. Vol. 17, No. 4, pp. 683-689, 1987   DOI   ScienceOn
16 P. H. Krishnaiah and L. N. Kanal, editors. Classification, pater recognition, and reduction of dimensionality, Vol. 2 of Handbook of Statistics. North-Holland, Amsterdam, 1982
17 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', Pater Recognition Vol. 35, pp. 659-675, 2002   DOI   ScienceOn
18 P.P. Rui, D. Antonio, 'Interpretability and learning in neuro-fuzzy systems,' Fuzzy Sets and Systems. Vol. 147, pp-17-38, 2004   DOI   ScienceOn
19 L. X. Wang, J. M. Mendel, 'Generating fuzzy rules from numerical data with applications,' IEEE Trans. Systems, Man, Cyber., Vol. 22, No. 6, pp. 1414-1427, 1992   DOI   ScienceOn
20 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
21 J.C. Bezdek, 'Pattern Recognition with Fuzzy Objective Function Algorithms,' Plenum, New York, 1981