Evolutive Identification of Fuzzy Systems for Time-Series Prediction

  • Jesús González
  • Ignacio Rojas
  • Héctor Pomares
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

This paper presents a new algorithm for designing fuzzy systems. It automatically identifies the optimum number of rules in the fuzzy knowledge base and adjusts the parameters defining them.

This algorithm hybridizes the robustness and capability of evolutive algorithms with multiobjective optimization techniques which are able to minimize both the prediction error of the fuzzy system and its complexity, i.e. the number of parameters. In order to guide the search and accelerate the algorithm’s convergence, new specific genetic operators have been designed, which combine several heuristic and analytical methods. The results obtained show the validity of the proposed algorithm for the identification of fuzzy systems when applied to time-series prediction.

Keywords

Fuzzy systems evolution multiobjective optimization 
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References

  1. 1.
    H. Akaike. Information Theory and Extension of the Maximum Likelihood Principle, pages 267–810. Akademia Kiado, Budapest, 1973.Google Scholar
  2. 2.
    V. Chankong and Y. Y. Haimes. Multiobjective Decision Making Theory and Methodology. North-Holland, New York, 1983.Google Scholar
  3. 3.
    S. Chen, C. F. N. Cowan, and P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks, 2:302–309, 1991.CrossRefGoogle Scholar
  4. 4.
    K. Deb and D. E. Goldberg. An investigation of niches and species formation in genetic function optimization. In J. D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 42–50, San Mateo, CA, 1991. Morgan Kaufmann.Google Scholar
  5. 5.
    C. M. Fonseca and P. J. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In S. Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, San Mateo, CA, 1993. Morgan Kaufmann.Google Scholar
  6. 6.
    D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.Google Scholar
  7. 7.
    D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette, editor, Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pages 41–49, San Mateo, CA, 1987. Morgan Kaufmann.Google Scholar
  8. 8.
    A. E. Hans. Multicriteria optimization for highly accurate systems. In W. Stadler, editor, Multicriteria Optimization in Engineering and Sciences, Mathematical Concepts and Methods in Science and Engineering, volume 19, pages 309–352, New York, 1988. Plenum Press.Google Scholar
  9. 9.
    J. J. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.Google Scholar
  10. 10.
    J. S. R. Jang. Anfis: Adaptive network-based fuzzy inference system. IEEE Trans. Syst., Man, Cybern., 23:665–685, May 1993.Google Scholar
  11. 11.
    J. S. R. Jang, C. T. Sun, and E. Mizutani. Neuro-Fuzzy and Soft Computing. Prentice-Hall, ISBN 0-13-261066-3, 1997.Google Scholar
  12. 12.
    D. Kim and C. Kim. Forecasting time series with genetic fuzzy predictor ensemble. IEEE Trans. Fuzzy Systems, 5(4):523–535, Nov. 1997.CrossRefGoogle Scholar
  13. 13.
    Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, 3rd edition, 1996.Google Scholar
  14. 14.
    H. Pomares, I. Rojas, J. Ortega, J. González, and A. Prieto. A systematic approach to a self-generating fuzzy rule-table for function approximation. IEEE Trans. Syst. Man and Cyber.—Part B, 30(3):431–447, 2000.CrossRefGoogle Scholar
  15. 15.
    J. Rissanen. Modelling by shortest data description. Automatica, 14:464–471, 1978.CrossRefGoogle Scholar
  16. 16.
    I. Rojas, H. Pomares, J. Ortega, and A. Prieto. Self-organized fuzzy system generation from training examples. IEEE Trans. Fuzzy Systems, 8(1), Feb. 2000.Google Scholar
  17. 17.
    J. D. Schaffer. Some Experiments in Machine Learning using Vector Evaluated Genetic Algorithms. Ph.d. dissertation, Vanderbilt University, Nashville, TN, 1984. TCGA file No. 00314.Google Scholar
  18. 18.
    N. Srinivas and K. Dev. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3):221–248, 1995.CrossRefGoogle Scholar
  19. 19.
    L. X. Wang and J. M. Mendel. Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Trans. Neural Networks, 3:807–814, 1992.CrossRefGoogle Scholar
  20. 20.
    L. X. Wang and J. M. Mendel. Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man and Cyber., 22(6):1414–1427, November/December 1992.CrossRefMathSciNetGoogle Scholar
  21. 21.
    B. A. Whitehead and T. D. Choate. Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Trans. Neural Networks, 7(4):869–880, July 1996.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jesús González
    • 1
  • Ignacio Rojas
    • 1
  • Héctor Pomares
    • 1
  1. 1.Department of Computer Architecture and Computer TechnologyE. T. S. Ingeniería Informática University of GranadaGranadaSpain

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