A basic approach to reduce the complexity of a self-generated fuzzy rule-table for function approximation by use of symbolic interpolation

Authors:
G. Rubio;H. Pomares
Affiliations:
Department of Computer Architecture and Computer Technology, University of Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

There are many papers in the literature that deal with the problem of the design of a fuzzy system from a set of given training examples. Those who get the best approximation accuracy are based on TSK fuzzy rules, which have the problem of not being as interpretable as Mamdany-type Fuzzy Systems. A question now is posed: How can the interpretability of the generated fuzzy rule-table base be increased? A possible response is to try to reduce the rule-base size by generalizing fuzzy-rules consequents which are symbolic functions instead of fixed scalar values or polynomials, and apply symbolic interpolations techniques in fuzzy system generation. A first approximation to this idea is presented in this paper for 1-D functions.