Methods for Simplification of Fuzzy Models

Uzay Kaymak, Robert Babuška, Magne Setnes, Henk B. Verbruggen and Hans R. van Nauta Lemke
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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

Chapter 4 in Intelligent Hybrid Systems, 1997, pp 91-108 from Springer

Abstract: 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.

Keywords: Rule Base; Fuzzy Cluster; Cluster Validity; Relevant Number; Fuzzy Cluster Algorithm (search for similar items in EconPapers)
Date: 1997
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DOI: 10.1007/978-1-4615-6191-0_4

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