Autostructuration of fuzzy systems by rules sensitivity analysis

Fuzzy Sets and Systems

Volumn 118, Issue 2, 2001, Pages 281-296

  Fiordaliso, Antonio ^a  

^a FACULTÉ POLYTECHNIQUE DE MONS   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; CHAOS THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; MEMBERSHIP FUNCTIONS; SENSITIVITY ANALYSIS; TIME SERIES ANALYSIS;

CHAOTIC TIME SERIES FORECASTING; FUZZY RULES LEARNING; TAKAGI-SUGENO SYSTEMS;

FUZZY SETS;

EID: 0005540091     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(98)00430-8     Document Type: Article

References (48)

1. Anderberg M.R. Cluster Analysis for Applications. 1973;Academic Press, New York.
2. R. Babuška, Fuzzy modeling and identification, Thesis, Delft University of Technology, 1996.
3. H. Bersini, A. Duchateau, N. Bradshaw, Using incremental learning algorithms in the search for minimal and effective fuzzy models, Proc. FUZZ-IEEE '97 Conf., 1997, pp. 1417-1422.
4. Bezdek J. Pattern recognition with fuzzy objective function algorithms. 1981;Plenum Press, New York.
5. Buckley J.J. Universal fuzzy controllers. Automat. J. 28:1992;1245-1248.
6. Buckley J.J., Hayashi Y. Numerical relationships between neural networks, continuous functions and fuzzy systems. Fuzzy Sets and Systems. 60:1993;1-8.
7. Castagli M. Nonlinear prediction of chaotic time series. Physica D. 35:1989;335-356.
8. Chiu S.L. Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Systems. 2:1994;267-278.
9. Combs W.E., Andrews J.E. Combinatorial rule explosion eliminated by a fuzzy rule configuration. IEEE Trans. Fuzzy Systems. 6:1998;1-11.
10. Farmer J.D., Sidorowich J.J. Predicting chaotic time series. Phys. Rev. Lett. 59:1987;845-848.
11. A. Fiordaliso, A pruning method for the self-structuring of fuzzy systems applied to function approximation, Proc. EUFIT96 Conf., vol. 1, 1996, pp. 581-586.
12. Fiordaliso A. A nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems. Internat. J. Forecasting. 14:1998;367-379.
13. A. Fiordaliso, Contribution des systèmes neuronaux et flous à la prévision des séries temporelles, Thesis, University of Mons-Hainaut, 1998.
14. Gath I., Geva A.B. Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11:1989;773-781.
15. Gershenfeld N.A., Weigend A.S. The future of time series. learning and understanding Gershenfeld N.A., Weigend A.S. Time Series Prediction. Forecasting the Future and Understanding the Past:1993;1-70 Addison-Wesley, Palo Alto.
16. Girosi F., Jones M., Poggio T. Regularization Theory and Neural Networks Architectures. Neural Comput. 7:1995;219-269.
17. V. Gorrini, T. Salomé, H. Bersini, Self-structuring systems for function approximation, Proc. FUZZ-IEEE / IFES'95, 1995, pp. 531-536.
18. E.E. Gustafson, W.C. Kessel, Fuzzy clustering with a fuzzy covariance matrix, Proc. IEEE CDC, 1979, pp. 761-766.
19. Hartani R., Nguyen H.T., Bouchon-Meunier B. Sur l'approximation universelle des systèmes flous. RAIRO-APII-JESA. 30:1996;645-663.
20. Hataway R., Bezdek J. Switching regression models and fuzzy clustering. IEEE Trans. Fuzzy Systems. 1:1993;195-204.
21. Hinton G. Machine learning: paradigms and methods. Artificial Intell. 40:1989;185-234.
22. Hübner U., Abraham N.B., Weiss C.O. Dimensions and entropies of chaotic intensity pulsations in a single-mode far-infrared NH3 laser. Phys. Rev. A. 40:1989;6354-6365.
23. Jacobs R.A., Jordan M.I., Nowlan S.J., Hinton G.E. Adaptive mixture of local experts. Neural Comput. 3:1991;79-87.
24. C.L. Karr, Design of an adaptive fuzzy logic controller using a genetic algorithm, Proc. 4th Int. Conf. Genetic Algorithms, 1991, pp. 450-457.
25. Kosko B. Fuzzy systems as universal approximators. IEEE Trans. Comput. 43:1994;1329-1333.
26. Kreinovich V.Y. Arbitrary nonlinearity is sufficient to represent all functions by neural networks: a theorem. Neural Networks. 4:1991;381-383.
27. Kurkova V. Kolmogorov's theorem is relevant. Neural Comput. 3:1991;617-622.
28. Kurkova V. Kolmogorov's theorem and multilayer neural networks. Neural Networks. 5:1992;501-506.
29. Lawson C.L., Hanson R.J. Solving Least Squares Problems. 1974;Prentice-Hall, New York.
30. Lin C.T., Lee C.S.G. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems. IEEE Trans. Fuzzy Systems. 2:1994;46-63.
31. D. Lowe, Adaptive radial basis function nonlinearities and the problem of generalization, Proc. 1st IEEE Internat. Conf. on Artificial Neural Networks, 1989, pp. 171-175.
32. Marquardt D.W. An algorithm for least squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11:1963;431-441.
33. McLachlan B., Basford K. Mixture Models. Inference and Applications to Clustering:1988;Dekker, Basel.
34. H.T. Nguyen, V. Kreinovich, On approximation of controls by fuzzy systems, Proc. 5th IFSA, 1993, pp. 1414-1417.
35. H. Nomura, I. Hayashi, N. Wakami, A learning method of fuzzy inference rules by descent method, Proc. IEEE Int. Conf. Fuzzy Systems, 1992, pp. 203-210.
36. Platt J. Resource-allocating network for function interpolation. Neural Comput. 3:1991;213-225.
37. Poggio T., Girosi F. Networks for approximation and learning. Proc. IEEE. 78:1990;1481-1497.
38. Quandt R.E., Ramsey J.B. A new approach to estimating switching regressions. J. Amer. Statist. Soc. 67:1972;306-310.
39. Schwarz G. Estimating the dimension of a model. Ann. Statist. 6:1978;461-464.
40. Sugihara G., May R.M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature. 344:1990;734-741.
41. Sun C.T. Rulebase structure identification in an adaptive network based fuzzy inference system. IEEE Trans. Fuzzy Systems. 2:1994;64-73.
42. Takagi T., Sugeno M. Fuzzy identification of systems and its application to modelling and control. IEEE Trans. Systems Man Cybernet. 15:1985;116-132.
43. Takens F. Detecting strange attractors in turbulence. Rand D., Young L. Lecture Notes in Mathematics. vol. 898:1981;336-381 Springer, Berlin.
44. Teran R., Draye J.P., Pavisic D., Calderon G., Libert G. Predicting a chaotic time series using a dynamical recurrent neural network. Mertzios B.G., Liatsis P. Proc. IWISP '96. 1996;115-118 Elsevier Science, Amsterdam.
45. L.-X. Wang, Fuzzy systems are universal controllers, Proc. IEEE Internat. Conf. on Fuzzy Systems, 1992, pp. 1163-1170.
46. Wang L.-X., Mendel J.M. Fuzzy basis functions, universal approximation and orthogonal least-squares learning. IEEE Trans. Neural Networks. 3:1992;807-814.
47. Weigend A., Huberman B., Rumelhart D. Predicting sunspots and exchange rates with connectionnist methods. Castagli M., Eubank S. Nonlinear Modelling and Forecasting. Addison-Wesley, New York1992;395-432.
48. Zeng X., Singh M.J. Approximation theory of fuzzy systems. SISO case IEEE Trans. Fuzzy Systems. 2:1994;162-176.