Input selection and partition validation for fuzzy modelling using neural network

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. ( 1999 Elsevier Science B.V. All rights reserved.