A neuro-fuzzy method to learn fuzzy classification rules from data

Abstract Neuro-fuzzy systems have recently gained a lot of interest in research and application. Neuro-fuzzy models as we understand them are fuzzy systems that use local learning strategies to learn fuzzy sets and fuzzy rules. Neuro-fuzzy techniques have been developed to support the development of e.g. fuzzy controllers and fuzzy classifiers. In this paper we discuss a learning method for fuzzy classification rules. The learning algorithm is a simple heuristics that is able to derive fuzzy rules from a set of training data very quickly, and tunes them by modifying parameters of membership functions. Our approach is based on NEFCLASS, a neuro-fuzzy model for pattern classification. We also discuss some results obtained by our software implementation of NEFCLASS, which is freely available on the Internet.

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