Advanced Inference Filter Defuzzification (original) (raw)

Abstract

In the field of modeling, fuzzy models are one efficient approach for representing technical systems or human control strategies. Fuzzy models have the advantage of supplying a transparent and interpretable model. Conventional fuzzy models are based on fuzzification, inference and defuzzification. The fuzzification and inference operations are theoretically well-established in the framework of fuzzy logic. In contrast, conventional defuzzification methods are essentially empirically motivated. First, we recapitulate the inference filter concept, which supplies a new understanding of the defuzzification process and a theoretical framework. Second, we extend this approach to the advanced inference filter concept, which leads to a defuzzification method that is better suited to imitate the behavior of a human expert.

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. D. Driankov, H. Hellendoorn, and M. Reinfrank. An Introduction to Fuzzy Control. Springer, Berlin Heidelberg, 1993.
    MATH Google Scholar
  2. H. Kiendl. The inference filter. In Second European Congress on Intelligent Techniques and Soft Computing (EUFIT’ 94), volume 1, pages 443–452, Aachen, 1994. Verlag Mainz.
    Google Scholar
  3. H. Kiendl. Verfahren zur Defuzzifizierung für signalverarbeitende Fuzzy-Baueinheiten und Filtereinrichtungen hierfür. Kiendl, 1995. Patent DE 44 16 465.
    Google Scholar
  4. H. Kiendl. Fuzzy-Control methodenorientiert. Oldenbourg Verlag, München, 1997.
    Google Scholar
  5. H. Kiendl. Non-translation-invariant defuzzification. In Proceedings of the Sixth IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’ 97), Barcelona, Spanien, 1997, pages 737–742, Piscataway, NJ, 1997. IEEE Press.
    Google Scholar
  6. H. Kiendl, R. Knicker, and F. Niewels. Two way fuzzy controllers based on hyperinference and inference filter. In Proceedings of World Automation Congress (WAC’ 96), Montpellier, Frankreich, 1996. TSI Enterprise Inc.
    Google Scholar
  7. H. Kiendl and P. Krause. Implicit modeling to cope with ambigous data. In Proceedings of the IASTED International Conference Modeling, Identification and Control (MIC 2001), Innsbruck, Österreich, volume 1, pages 291–296, 2001.
    Google Scholar
  8. A. Krone. Datenbasierte Generierung von relevanten Fuzzy-Regeln zur Modellierung von Prozesszusammenhängen und Bedienstrategien. Fortschritt-Berichte VDI, Reihe 10, Nr. 615. VDI Verlag, Düsseldorf, 1999.
    Google Scholar
  9. M. Mackey and L. Glass. Oscillation and Chaos in Physiological Control Systems. Science, 197:287–289, 1977.
    Article Google Scholar
  10. F. Niewels. Analytische formeln für das inferenzfilter zur berechnung gefilterter zugehöorigkeitsfunktionen. pages 1–14. Forschungsbericht der Fakultät für Elektrotechnik, Nr. 0896, Universität Dortmund, 1996.
    Google Scholar
  11. J. Praczyk. Entwurf von Fuzzy-Gütemaßen zur Prozeßbewertung. Fortschritt-Berichte VDI, Reihe 8, Nr. 796. VDI Verlag, Düsseldorf, 1999.
    Google Scholar
  12. G. Reil and H. Jessen. Fuzzy contour modelling of roll bent components using inference filter. In Proceedings of the Third European Congress on Intelligent Techniques and Soft Computing (EUFIT’ 95), pages 771–774, Aachen, 1995. Verlag Mainz.
    Google Scholar
  13. T. Slawinski, A. Krone, P. Krause, and H. Kiendl. The fuzzy-rosa method: A statistically motivated fuzzy approach for data-based generation of small interpretable rule bases in high-dimensional search spaces. In M. Last, A. Kandel, and H. Bunke, editors, Data Mining and Computational Intelligence, pages 141–166. Physica-Verlag Heidelberg, 2001.
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. Chair of Electrical Control Engineering, University of Dortmund, Germany
    H. Kiendl & P. Krause

Authors

  1. H. Kiendl
  2. P. Krause

Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, 44221, Dortmund, Germany
    Bernd Reusch

Rights and permissions

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kiendl, H., Krause, P. (2001). Advanced Inference Filter Defuzzification. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4\_29

Download citation

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publish with us