Skip to main content
SpringerLink
Log in
Book cover

Fuzzy Modelling pp 375–391Cite as

Rule-Based Forecasting

  • Chapter

Part of the book series: International Series in Intelligent Technologies ((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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. G. E. P. Box and G. M. Jenkins, Time Series Analysis Forecasting and Control (Englewood Cliffs, Prentice Hall, 1976).

    MATH  Google Scholar 

  2. M. Casdagli and S. Eubank, editors, Nonlinear Modeling and Forecasting,Proceedings Vol. XII, Santa Fe Institute (Reading, Addison-Wesley, 1992).

    Google Scholar 

  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. 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. B. P. Graham and R. B. Newell, “Fuzzy adaptive control of a first-order process,” Fuzzy Sets and Systems,31, 47–65 (1989).

    Article  MathSciNet  MATH  Google Scholar 

  6. L. Feng and X. X. Guang, “A forecasting model of fuzzy self-regression,” Fuzzy Sets and Systems,38, 239–242 (1993).

    Article  Google Scholar 

  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).

    Article  MathSciNet  Google Scholar 

  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. 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. 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. L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Trans. Systems, Man and Cybernetics 22, 1414–1427 (1992).

    Article  MathSciNet  Google Scholar 

  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. 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).

    Article  Google Scholar 

  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. 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. W. Pedrycz, Fuzzy Sets Engineering (Boca Raton, CRC Press, 1995).

    MATH  Google Scholar 

  17. D. Driankov, H. Hellendoorn, and M. Reinfrank, An Introduction to Fuzzy Control (New York, Springer, 1993).

    MATH  Google Scholar 

  18. R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control (New York, Wiley, 1994).

    Google Scholar 

  19. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (New York, Wiley-Interscience, 1973).

    MATH  Google Scholar 

  20. Y. H. Pao, Adaptive Pattern Recognition and Neural Networks,(Reading, Addison-Wesley, 1989).

    MATH  Google Scholar 

  21. D. Specht, “Probabilistic Neural Networks,” Neural Networks,3,109–118 (1990).

    Article  Google Scholar 

  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).

    Article  MathSciNet  Google Scholar 

  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. E. Ott, “Strange attractors and chaotic motions of dynamical systems,” Rev. Mod. Phys. 53,655–671 (1981).

    Article  MathSciNet  MATH  Google Scholar 

  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. W Pedrycz, “Fuzzy modelling: Fundamentals, construction and evaluation,” Fuzzy Sets and Systems,41,1–15 (1991).

    Article  MathSciNet  MATH  Google Scholar 

  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. Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series,” Fuzzy Sets and Systems,62,1–8 (1994).

    Article  Google Scholar 

  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. 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. W. Pedrycz, “Genetic algorithms for learning in fuzzy relational structures,” Fuzzy Sets and Systems,69,37–52 (1995).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Los Alamos National Laboratory, MS E541, Los Alamos, NM, 87545, USA

    Andrew Zardecki

Authors
  1. Andrew Zardecki
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, University of Manitoba, Winnipeg, R3T 2N2, Canada

    Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Kluwer Academic Publishers

About this chapter

Cite this chapter

Zardecki, A. (1996). Rule-Based Forecasting. In: Pedrycz, W. (eds) Fuzzy Modelling. International Series in Intelligent Technologies, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1365-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-1365-6_17

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8589-2

  • Online ISBN: 978-1-4613-1365-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation