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Methods for Simplification of Fuzzy Models

In: Intelligent Hybrid Systems

Author

Listed:
  • Uzay Kaymak

    (Control Laboratory, Dept of Electrical Engineering Delft University of Technology)

  • Robert Babuška

    (Control Laboratory, Dept of Electrical Engineering Delft University of Technology)

  • Magne Setnes

    (Control Laboratory, Dept of Electrical Engineering Delft University of Technology)

  • Henk B. Verbruggen

    (Control Laboratory, Dept of Electrical Engineering Delft University of Technology)

  • Hans R. van Nauta Lemke

    (Control Laboratory, Dept of Electrical Engineering Delft University of Technology)

Abstract

Redundancy may be present in fuzzy models which are acquired from data by using techniques like fuzzy clustering and gradient learning. The redundancy may manifest itself in the form of a larger number of rules than necessary, or in the form of fuzzy sets that are very similar to one another. By reducing this redundancy, transparent fuzzy models with appropriate number of rules and distinct fuzzy sets are obtained. This chapter considers cluster validity and cluster merging techniques for determining the relevant number of rules for a given application when fuzzy clustering is used for modeling. Similarity based rule base simplification is then applied for reducing the number of fuzzy sets in the model. The techniques lead to transparent fuzzy models with low redundancy.

Suggested Citation

  • Uzay Kaymak & Robert Babuška & Magne Setnes & Henk B. Verbruggen & Hans R. van Nauta Lemke, 1997. "Methods for Simplification of Fuzzy Models," Springer Books, in: Da Ruan (ed.), Intelligent Hybrid Systems, chapter 4, pages 91-108, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-6191-0_4
    DOI: 10.1007/978-1-4615-6191-0_4
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