Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling: A New Approach

IEEE Transactions on Fuzzy Systems

Volumn 12, Issue 1, 2004, Pages 1-12

  Chiang, Jung Hsien ^a   Hao, Pei Yi ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Fuzzy basis function; Fuzzy modeling; Kernel function; Support vector machine

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; ERROR ANALYSIS; GENETIC ALGORITHMS; HEURISTIC METHODS; INTEGRATION; LAGRANGE MULTIPLIERS; LEARNING SYSTEMS; MAPPING; MATRIX ALGEBRA; NEURAL NETWORKS; PROBLEM SOLVING; REGRESSION ANALYSIS;

FUZZY BASIS FUNCTION; FUZZY MODELING; KERNEL FUNCTION; SUPPORT VECTOR MACHINE;

FUZZY CONTROL;

EID: 1542333735     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2003.817839     Document Type: Article

References (38)

1. S. Abe and M. S. Lan, "A method for fuzzy rules extraction directly from numerical data and its application to pattern classification," IEEE Trans. Fuzzy Syst., vol. 3, pp. 353-361, Feb. 1995.
2. E. B. Baum and K. J. Lang, "Constructing hidden units using examples and queries," in Advances in Neural Information Processing Systems. San Mateo, CA: Morgan Kaufmann, 1991, vol. 3, pp. 904-910.
3. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifier," in Proc. 5th ACM Workshop Computational Learning Theory, Pittsburgh, PA, July 1992, pp. 144-152.
4. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowledge Disc., vol. 2, no. 2, pp. 955-974, 1998.
5. C. Cortes and V. N. Vapnik, "Support vector network," Mach. Learn., vol. 20, pp. 273-297, 1995.
6. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, U.K.: Cambridge Univ. Press, 2000.
7. J. A. Dickerson and B. Kosko, "Fuzzy function approximation with ellipsoidal rules," IEEE Trans. Syst., Man, Cybern., vol. 26, pp. 542-560, Aug. 1996.
8. R. Fisher, "The use of multiple measurements in taxonomic problems," Ann. Eugenics, pt. II, vol. 7, pp. 179-188, 1936.
9. J. Hauser, S. Satry, and P. Kokotovic, "Nonlinear control via approximate input-output linearization: The ball and beam example," IEEE Trans. Automat. Contr., vol. 37, pp. 392-398, Mar. 1992.
10. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedorward networks are universal approximators," Neural Networks, vol. 2, pp. 359-366, 1989.
11. H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka, "Selecting fuzzy IF-THEN rules for classification probems using genetic algorithms," IEEE Trans. Fuzzy Syst., vol. 3, pp. 260-270, Aug. 1995.
12. J.-S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23, pp. 665-685, May/June 1993.
13. C.-F. Juang and C.-T. Lin, "An on-line self-constructing neural fuzzy inference network and its applications," IEEE Trans. Fuzzy Syst., vol. 6, pp. 12-32, Feb. 1998.
14. J. M. Keller, R. Krishnapuram, and F. C.-H. Rhee, "Evidence aggregation networks for fuzzy logic inference," IEEE Trans. Neural Networks, vol. 3, pp. 761-769, Sept. 1992.
15. H. M. Kim and J. M. Mendel, "Fuzzy basis functions: Comparisons with other basis fonctions," IEEE Trans. Fuzzy Syst., vol. 3, pp. 158-168, May 1995.
16. B. Kosko, Neural Networks and Fuzzy Systems. Upper Saddle River, NJ: Prentice-Hall, 1992a.
17. _, "Fuzzy systems as universal approximators," Proc. IEEE Int. Conf. Fuzzy Syst., pp. 1153-1162, 1992b.
18. K. J. Lang and M. J. Witbrock, "Learning to tell two spirals apart," in Proc. 1988 Connectionst Models Summer School, 1989, pp. 52-59.
19. C.-C. Lee, "Fuzzy logical in control systems: Fuzzy logic controller, part I," IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 404-418, Mar./Apr. 1990.
20. C.-J. Lin and C.-T. Lin, "An ART-based fuzzy adaptive learning control network," IEEE Trans. Fuzzy Syst., vol. 5, pp. 477-496, Nov. 1997.
21. 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.
22. Z.-Q. Liu and F. Yan, "Fuzzy neural network in case-based diagnostic system," IEEE Trans. Fuzzy Syst., vol. 5, pp. 209-222, 1997.
23. M. C. Mackey and L. Glass, "Oscillation and chaos in physiological control systems," Science, vol. 197, pp. 287-289, July 1977.
24. J. M. Mendel, "Fuzzy logic systems for engineering: A tutorial," Proc. IEEE, vol. 83, pp. 345-377, Mar. 1995.
25. G. C. Mouzouris and J. M. Mendel, "A single-value-QR decomposition based method for training fuzzy logic systems in uncertain environments," J. Intell. Fuzzy Syst., vol. 55, pp. 367-374, 1997.
26. J. Nie and D. A. Linkens, "Learning control using fuzzified self-organizing radial basis function network," IEEE Trans. Fuzzy Syst., vol. 1, pp. 280-287, Aug. 1993.
27. E. Osuna, R. Freund, and F. Girosi, "Training support vector machines: An application to face detection," in Proc. Comput. Vision Patt. Recogn., 1997, pp. 130-136.
28. W. Pedrycz and M. Reformat, "Rule based modeling of nonlinear relationships," IEEE Trans. Fuzzy. Syst., vol. 5, pp. 256-269, May 1997.
29. P. K. Simpson, "Fuzzy min-max neural networks - Part 1: Classification," IEEE Trans. Neural Networks, vol. 3, pp. 776-786, Sept. 1992.
30. A. J. Smola, B. Schölkopf, and K.-R. Muller, "The connection between regularization operators and support vector kernels," Neural Networks, vol. 11, pp. 637-649, 1998.
31. A. J. Smola and B. Schölkopf, "A tutorial on support vector regression,", NeuroCOLT2 Tech. Rep. NC2-TR-1998-030, GMD, 1998b.
32. C.-T. Sun, "Rule base structure identification in an adaptive network based fuzzy inference system," IEEE Trans. Fuzzy Syst., vol. 2, pp. 64-73, Apr. 1994.
33. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst., Man, and Cybern., vol. 1, pp. 116-132, Jan. 1985.
34. H. Takagi and I. Hayashi, "NN-driven fuzzy reasoning," Int. J. Approx. Reason., vol. 5, pp. 191-212, 1991.
35. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
36. L. X. Wang and J. M. Mendel, "Fuzzy basis functions, universal approximation, and orthogonal least-squares learning," IEEE Trans. Neural Networks, vol. 3, pp. 807-814, Sept. 1992a.
37. L. X. Wang, "Fuzzy systems are universal approximators," Proc. IEEE Int. Conf. Fuzzy Systems, pp. 1163-1170, Mar. 1992b.
38. L. X. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples," IEEE Trans. Syst., Man, Cybern., vol. 22, pp. 1414-1427, Nov./Dec. 1992c.