✦ LIBER ✦

Input selection and partition validation for fuzzy modelling using neural network

✍ Scribed by D.A. Linkens; Min-You Chen

Publisher: Elsevier Science
Year: 1999
Tongue: English
Weight: 247 KB
Volume: 107
Category: Article
ISSN: 0165-0114
DOI: 10.1016/s0165-0114(97)00322-9

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✦ Synopsis

A simple and e!ective method for selecting signi"cant input variables and determining optimal number of fuzzy rules when building a fuzzy model from data is proposed. In contrast to the existing clustering-based methods, in this approach both input selecting and partition validating are determined on the basis of a class of sub-clusters created by a self-organising network instead of on the data. The important input variables which independently and signi"cantly in#uence the system output can be extracted by a fuzzy neural network. On the other hand, the optimal number of fuzzy rules can be determined separately via the fuzzy c-means algorithm with a modi"ed fuzzy entropy as the criterion of cluster validation. The simulation results show that the proposed method can provide good model structures for fuzzy modelling and has high computing e$ciency.

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