Fuzzy Control pp 177-185 | Cite as

Inference Methods for Partially Redundant Rule Bases

  • Ralf Mikut
  • Jens Jäkel
  • Lutz Gröll
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 6)


In this paper, a new inference strategy applicable to redundant or contradictory fuzzy rules is introduced. Both characteristics result mainly from a data-based generation of fuzzy systems where linguistic hedges are used to get an abstract description and where different rules’ premises are overlapping. It is shown, that common fuzzy operators fail in these cases and that the newly introduced switching fuzzy operators solve these problems.


Rule Base Linguistic Term Inference Method Positive Small Primary Term 
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  1. 1.
    E. B. Hunt, J. Marin, P. T. Stone: Experiments in Induction. Academic Press, New York, (1966)Google Scholar
  2. 2.
    A. Krone, H. Kiendl: Automatic generation of positive and negative rules for two-way fuzzy controllers. In Proc. 2nd Europ. Congr. on Intelligent Techniques and Soft Computing EUFIT’94, Aachen (1994) 438–442Google Scholar
  3. 3.
    M. Fritsch: Baumorientierte Regel-Induktionsstrategie für das ROSA-Verfahren zur Modellierung komplexer dynamischer Systeme. Fortschritt-Bericht VDI, Reihe 8, Nr. 565. VDI-Verlag, Düsseldorf, (1996)Google Scholar
  4. 4.
    L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone: Classification and Regression Trees. Wadsworth, Belmont, Ca. (1984)Google Scholar
  5. 5.
    J. R. Quinlan: Induction of decision trees. Machine Learning 1 (1986) 81–106Google Scholar
  6. 6.
    J. Jäkel, L. Gröll, R. Mikut: Automatic generation and evaluation of interpretable rule bases for fuzzy systems. In Computational Intelligence for Modelling, Control and Automation CIMCA’99, IOS Press, Amsterdam (1999) 192–197Google Scholar
  7. 7.
    J. Jäkel, L. Gröll, R. Mikut: Tree-oriented hypothesis generation for interpretable fuzzy rules. In Proc. 7th Europ. Congr. on Intelligent Techniques and Soft Computing EUFIT’99, Aachen (1999)Google Scholar
  8. 8.
    J. Jäkel, L. Gröll, and R. Mikut: Bewertungsmaße zum Generieren von Fuzzy-Regeln unter Beachtung linguistisch motivierter Restriktionen. In Berichtsband 8. Workshop Fuzzy Control d. GMA-FA 5.22, Aachen (1998) 15–28Google Scholar
  9. 9.
    L. A. Zadeh: A fuzzy set theoretic interpretation of linguistic hedges. Journal of Cybernetics 2 (1972) 4–34MathSciNetCrossRefGoogle Scholar
  10. 10.
    N. Sano, I. Koyauchi, H. Kadotani, R. Takahashi: Transformation of membership functions by hedge and primitive terms using the extension principle. In Proc. 5th Zittau Fuzzy Colloquium (1997) 13–16Google Scholar
  11. 11.
    G. J. Klir, B. Yuan: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, Upper Saddle River, NJ, (1995)MATHGoogle Scholar
  12. 12.
    J. Jäkel, R. Mikut, H. Malberg, G. Bretthauer: Datenbasierte Regelsuche für Fuzzy-Systeme mittels baumorientierter Verfahren. In Berichtsband 9. Workshop Fuzzy Control d. GMA-FA 5.22, Dortmund (1999) 1–15Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ralf Mikut
    • 1
  • Jens Jäkel
    • 1
  • Lutz Gröll
    • 1
  1. 1.Institute for Applied Computer Science (IAI)Forschungszentrum Karlsruhe GmbHKarlsruheGermany

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