Input selection and partition validation for fuzzy modelling using neural network

A simple and effective method for selecting significant 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 significantly influence 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 modified 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 efficiency. (C) 1999 Elsevier Science B.V. All rights reserved.