Advertisement

Rule-Based Forecasting

  • Andrew Zardecki
Chapter
Part of the International Series in Intelligent Technologies book series (ISIT, volume 7)

Abstract

Fuzzy rule-based systems and related techniques, chiefly fuzzy basis functions expansions, are applied to time series forecasting and anomaly detection in temporal and spatial patterns. The usefulness of different techniques is compared using the simple parity classification problem as an example. Forecasting of a time series is analyzed, together with a brief discussion of chaotic and noisy patterns. As a by-product of the rule-based forecasting, an edge detection algorithm for digital images is obtained.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    G. E. P. Box and G. M. Jenkins, Time Series Analysis Forecasting and Control (Englewood Cliffs, Prentice Hall, 1976).zbMATHGoogle Scholar
  2. [2]
    M. Casdagli and S. Eubank, editors, Nonlinear Modeling and Forecasting,Proceedings Vol. XII, Santa Fe Institute (Reading, Addison-Wesley, 1992).Google Scholar
  3. [3]
    A. S. Weigand and N. A. Gershenfeld, editors, Time Series Prediction,Proceedings Vol. XV, Santa Fe Institute (Reading, Addison-Wesley, 1993).Google Scholar
  4. [4]
    A. Lapedes and R. Farber, “Nonlinear signal processing using neural networks: prediction and system modeling,” Los Alamos National Laboratory document LA-UR-87–2662 (July 1987).Google Scholar
  5. [5]
    B. P. Graham and R. B. Newell, “Fuzzy adaptive control of a first-order process,” Fuzzy Sets and Systems,31, 47–65 (1989).MathSciNetzbMATHCrossRefGoogle Scholar
  6. [6]
    L. Feng and X. X. Guang, “A forecasting model of fuzzy self-regression,” Fuzzy Sets and Systems,38, 239–242 (1993).CrossRefGoogle Scholar
  7. [7]
    J. J. Saade and H. Schwarzlander, “Application of fuzzy hypothesis testing to signal detection under uncertainty,” Fuzzy Sets and Systems,62, 9–19 (1994).MathSciNetCrossRefGoogle Scholar
  8. [8]
    J. Nie “A fuzzy-neural approach to time series prediction,” in Proceedings of IEEE International Conference on Neural Networks (Piscataway, NJ, IEEE Service Center, 1994), pp. 3164–3169.Google Scholar
  9. [9]
    A. Satyadas and H. C. Chen, “An application of intelligent neural networks to time series business fluctuation prediction.” in Proceedings of IEEE International Conference on Neural Networks (Piscataway, NJ, IEEE Service Center, 1994), pp. 3640–3645.Google Scholar
  10. [10]
    R. Kozma, M. Kitamura, M. Sakuma, and Y. Yokoyama, “Anomaly detection by neural network models and statistical time series analysis,” in Proceedings of IEEE International Conference on Neural Networks (Piscataway, NJ, IEEE Service Center, 1994), pp. 3207–3210.Google Scholar
  11. [11]
    L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Trans. Systems, Man and Cybernetics 22, 1414–1427 (1992).MathSciNetCrossRefGoogle Scholar
  12. [12]
    H. M. Kim and J. M. Mendel, “Fuzzy basis functions: comparisons with other basis functions,” University of Southern California report USC-SIPI #229 (January 1993).Google Scholar
  13. [13]
    L. X. Wang and J. M. Mendel, “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning,” IEEE Trans. Neural Networks,3,807–813 (1992).CrossRefGoogle Scholar
  14. [14]
    J. Hohensohn and J. M. Mendel, “Two-pass orthogonal least-squares algorithm to train and reduce fuzzy logic systems,” in Proceedings of IEEE International Conference on Fuzzy Systems (Piscat-away, NJ, IEEE Service Center, 1994), pp. 696–700.Google Scholar
  15. [15]
    A. Zardecki, “Fuzzy control for forecasting and pattern recognition in a time series,” in Proceedings of IEEE International Conference on Fuzzy Systems (Piscataway, NJ, IEEE Service Center, 1994), pp. 1815–1819.Google Scholar
  16. [16]
    W. Pedrycz, Fuzzy Sets Engineering (Boca Raton, CRC Press, 1995).zbMATHGoogle Scholar
  17. [17]
    D. Driankov, H. Hellendoorn, and M. Reinfrank, An Introduction to Fuzzy Control (New York, Springer, 1993).zbMATHGoogle Scholar
  18. [18]
    R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control (New York, Wiley, 1994).Google Scholar
  19. [19]
    R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (New York, Wiley-Interscience, 1973).zbMATHGoogle Scholar
  20. [20]
    Y. H. Pao, Adaptive Pattern Recognition and Neural Networks,(Reading, Addison-Wesley, 1989).zbMATHGoogle Scholar
  21. [21]
    D. Specht, “Probabilistic Neural Networks,” Neural Networks,3,109–118 (1990).CrossRefGoogle Scholar
  22. [22]
    J. S. Kim and H. S. Cho, “A fuzzy logic and neural network approach to boundary detection for noisy imagery,” Fuzzy Sets and System,65,141–159 (1994).MathSciNetCrossRefGoogle Scholar
  23. [23]
    F. Russo and G. Ramponi, “Edge detection by FIRE operators,” in Proceedings of IEEE International Conference on Fuzzy Systems (Piscataway, NJ, IEEE Service Center, 1994), pp. 249–253.Google Scholar
  24. [24]
    E. Ott, “Strange attractors and chaotic motions of dynamical systems,” Rev. Mod. Phys. 53,655–671 (1981).MathSciNetzbMATHCrossRefGoogle Scholar
  25. [25]
    P. Diamond, “Chaos and information loss in fuzzy dynamical systems,” in Neural and Fuzzy Systems,edited by S. Mitra, M. M. Gupta, and W. F. Kraske (Bellingham, SPIE Optical Engineering Press, 1994), pp. 3–27.Google Scholar
  26. [26]
    W Pedrycz, “Fuzzy modelling: Fundamentals, construction and evaluation,” Fuzzy Sets and Systems,41,1–15 (1991).MathSciNetzbMATHCrossRefGoogle Scholar
  27. [27]
    G. A. Carpenter and S. Grossberg, “Fuzzy ARTMAP: A synthesis of neural networks and fuzzy logic for supervised categorization and nonstationary prediction,” in Fuzzy Sets, Neural Networks, and Soft Computing,edited by R. R. Yager and L. A. Zadeh (New York, Van Nostrand Reinhold, 1994), pp. 126–165.Google Scholar
  28. [28]
    Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series,” Fuzzy Sets and Systems,62,1–8 (1994).CrossRefGoogle Scholar
  29. [29]
    M. M. Gupta and G. K. Knopf, “Fuzzy neural network approach to control systems,” in Analysis and Management of Uncertainty: Theory and Applications,edited by M. Ayyub, M. M. Gupta, and L. N. Kanal (Amsterdam, Elsevier, 1992), pp. 183–197.Google Scholar
  30. [30]
    H. Takagi, “Fusion techniques of fuzzy systems and neural networks, and fuzzy systems and genetic algorithms,” in Applications of Fuzzy Logic,edited by B. Bosacchi and J. C. Bezdek, SPIE Proceedings, Vol. 2061 (Bellingham, SPIE Optical Engineering Press, 1993), pp. 402–413.Google Scholar
  31. [31]
    W. Pedrycz, “Genetic algorithms for learning in fuzzy relational structures,” Fuzzy Sets and Systems,69,37–52 (1995).CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Andrew Zardecki
    • 1
  1. 1.Los Alamos National LaboratoryLos AlamosUSA

Personalised recommendations