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

✍ Scribed by Detlef Nauck; Rudolf Kruse

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 943 KB
• Volume: 89
• Category: Article
• ISSN: 0165-0114
• DOI: 10.1016/s0165-0114(97)00009-2

✦ Synopsis

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 Intemet. (~) 1997 Elsevier Science B.V.