Separated antecedent and consequent learning for Takagi-Sugeno fuzzy systems

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2006, Pages 2263-2269

  Botzheim, János ^a,b   Lughofer, Edwin ^c   Klement, Erich Peter ^c   Kóczy, László T ^a   Gedeon, Tamás D ^b  

^a BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

Author keywords

(recursive) least squares; Bacterial evolutionary algorithm; Levenberg Marquardt method; Takagi Sugeno fuzzy systems

Indexed keywords

BACTERIAL EVOLUTIONARY ALGORITHM; LEVENBERG-MARQUARDT METHOD; TAKAGI-SUGENO FUZZY SYSTEMS;

COMPUTATIONAL COMPLEXITY; EVOLUTIONARY ALGORITHMS; LEAST SQUARES APPROXIMATIONS; LINEAR SYSTEMS; PARAMETER ESTIMATION;

FUZZY SYSTEMS;

EID: 34250725862     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2006.1682014     Document Type: Conference Paper

References (23)

1. J. Botzheim, B. Hamori, L. Kóczy, and A. Ruano, "Bacterial algorithm applied for fuzzy rule extraction," in Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, IPMU 2002, Annecy, France, 2002, pp. 1021-1026.
2. N. E. Nawa and T. Furuhashi, "Fuzzy system parameters discovery by bacterial evolutionary algorithm," IEEE Trans. on Fuzzy Systems, vol. 7, pp. 608-616, 1999.
3. M. Burger, J. Haslinger, and U. Bodenhofer, "Tuning of fuzzy systems as an ill-posed problem," in Progress in Industrial Mathematics at ECMI 2000, ser. Mathematics in Industry, M. Anile, V. Capasso, and A. Greco, Eds. Springer, 2002, vol. 1, pp. 493-498.
4. R. Yager and D. Filev, "Generation of fuzzy rules by mountain clustering," Machine Intelligence Institute, lona College, New Rochelle, NY 10801, Tech. Rep. MII-1318R, 1994.
5. D. Marquardt, "An algorithm for least-squares estimation of nonlinear parameters," SIAM J. Appl. Math., vol. 11, pp. 431-441, 1963.
6. A. Ruano, C. Cabrita, J. Oliveira, L. Kóczy, and D. Tikk, "Supervised training algorithms for B-spline neural networks and fuzzy systems," in Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 2001.
7. J. Botzheim, C. Cabrita, L. Kóczy, and A. Ruano, "Estimating fuzzy membership functions parameters by the Levenberg-Marquardt algorithm," in Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2004, Budapest, Hungary, 2004, pp. 1667-1672.
8. _, "Fuzzy rule extraction by bacterial memetic algorithm," in Proceedings of IFSA 2005, Bejing, China, July 2005, pp. 1563-1568.
9. L. Wang, "Fuzzy systems are universal approximates," in Proceedings of the IEEE International Conference on Fuzzy Systems, 1992, pp. 1163-1169.
10. J.-S. Jang and C.-T. Sun, "Functional equivalence between radial basis function networks and fuzzy inference systems," IEEE Trans. on Neural Networks, vol. 4, pp. 156-159, 1993.
11. L. Kóczy, D.Tikk, and T. Gedeon, "On functional equivalence of certain fuzzy controllers and RBF type approximation schemes," International Journal of Fuzzy Systems, 2000.
12. R. Babuska, Fuzzy Modeling for Control. Boston: Kluwer Academic Publishers, 1998.
13. E. Lughofer and E. Klement, "FLEXFIS: A variant for incremental learning of Takagi-Sugeno fuzzy systems," in Proceedings of FUZZ-IEEE 2005, Reno, Nevada, U.S.A., 2005, pp. 915-920.
14. J. Holland, Adaption in Natural and Artificial Systems. Cambridge, Massachusetts: The MIT Press, 1992.
15. E. Ruspini, "A new approach to clustering," Information and Control, vol. 15, pp. 22-32, 1969.
16. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York, Berlin, Heidelberg, Germany: Springer Verlag, 2001.
17. P. Werbos, "Beyond regression: New tools for prediction and analysis in the behavioral sciences," Ph.D. dissertation, Appl. Math., Harvard University, USA, 1974.
18. L. Ljung, System Identification: Theory for the User. Upper Saddle River, New Jersey 07458: Prentice Hall PTR, Prentic Hall Inc., 1999.
19. J. Yen, L. Wang, and C. Gillespie, "Improving the interpretability of TSK fuzzy models by combining global learning and local learning," IEEE Trans. on Fuzzy Systems, vol. 6, no. 4, pp. 530-537, 1998.
20. E. Lughofer, E. Hüllermeier, and E. Klement, "Improving the interpretability of data-driven evolving fuzzy systems," in Proceedings of EUSFLAT 2005, Barcelona, Spain, 2005, pp. 28-33.
21. M. Stone, "Cross-validatory choice and assessment of statistical predictions," Journal of the Royal Statistical Society, vol. 36, pp. 111-147, 1974.
22. J.-S. Jang, "ANFIS: Adaptive-network-based fuzzy inference systems," IEEE Trans. Syst. Man Cybern., vol. 23, pp. 665-685, 1993.
23. E. Lughofer and U. Bodenhofer, "Incremental learning of fuzzy basis function networks with a modified version of vector quantization," in to appear in Proceedings of IPMU 2006, Paris, France, 2006.