Fuzzy neural network design using support vector regression for function approximation with outliers

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn 3, Issue , 2005, Pages 2763-2768

  Lin, Chin Teng ^a   Liang, Sheng Fu ^a   Yeh, Chang Moun ^a   Fan, Kan Wei ^a  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Adaptive fuzzy kernel; Function approximation; Fuzzy neural network; Support vector regression

Indexed keywords

APPROXIMATION THEORY; DATABASE SYSTEMS; ESTIMATION; FUNCTIONS; FUZZY CONTROL; FUZZY SETS; LEARNING SYSTEMS; REGRESSION ANALYSIS;

ADAPTIVE FUZZY KERNELS; FUNCTION APPROXIMATION; FUZZY NEURAL NETWORKS; SUPPORT VECTOR REGRESSION;

NEURAL NETWORKS;

EID: 27944509100     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. B. Gabrys and A. Bargiela "General fuzzy min-max neural network for clustering and classification," IEEE Trans. Neural Networks, Vol. 11, pp. 769-783, May 2000.
2. K. Nozaki, H. Ishibuchi, and H. Tanaka, "Adaptive fuzzy rule-based classification system," IEEE Trans. Fuzzy Syst., Vol. 4, pp. 238-250, Aug. 1996.
3. M. Figueiredo and F. Gomide, "Design of fuzzy systems using neurofuzzy networks," IEEE Trans. Neural Networks, Vol. 10, pp. 815-827, July 1999.
4. C. C. Chuang, S. F. Su, J. T. Jeng, and C. C. Hsiao, "Robust support vector regression network for function approximation with outliers," IEEE Trans. Neural Networks, Vol. 13, pp. 1322-1330, Nov. 2002.
5. M. Setnes, R. Babuska, and H. B. Verbruggen, "Rule-based modeling: precision and transparency," IEEE Trans. Syst., Man, Cybern. Pt C, Vol. 28, pp. 165-169, Feb. 1998.
6. C. T. Lin and C. S. G. Lee "Neural-network-based fuzzy logic control and decision system," IEEE Trans. Comput., Vol. 40, pp. 1320-1336, Dec. 1991.
7. J. Platt, "A resource allocating network for function interpolation," Neural Computat., Vol. 3, pp. 213-225, 1991.
8. V. N. Vapnik, The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1990.
9. B. Schölkopf, C. J. C. Burges, and A. J. Smola, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA: MIT Press, 1999.
10. B. Schölkopf, P. Simard, A. J. Smola, and V. N. Vapnik, "Prior knowledge in support vector kernels," Advances in Neural Information Processing Systems, Vol. 10, MIT Press, Cambridge, MA.
11. C. J. C. Burges, "Simplified support vector decision rules," in Proc. 13th Int. Conf. Machine Learning, L. Saitta, Ed. San Mateo, CA:Morgan Kaufmann, 1996, pp. 71-77.
12. B. Schölkopf, S. Mika, C. Burges, etc "Input space versus feature space in kernel-based methods," IEEE Trans. Neural Networks, Vol. 10, pp. 1000-1017, Sep. 1999.
13. J. S. R. Jang, C. T. Sun, and E. Mizutani, Newo-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, Upper Saddle River, NJ, 1997.
14. S. R. Gunn. (1999) Support vector regression-Matlab toolbox. Univ. Southampton, Southampton, U. K. [Online]. Available: http://kernel-machines.org.