Incremental learning of fuzzy basis function networks with a modified version of vector quantization

Autoren Edwin Lughofer
Ulrich Bodenhofer
Titel Incremental learning of fuzzy basis function networks with a modified version of vector quantization
Buchtitel Proc. 11th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IMPU 2006)
Typ in Konferenzband
Verlag Éditions E.D.K.
Band I
Ort Paris, France
Abteilung IDM
ISBN 2-84254-112-X
Monat July
Jahr 2006
Seiten 56-63
SCCH ID# 537
Abstract

In this paper an algorithm for data-driven incrementallearning of fuzzy basis function networks is presented. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The premisepart learning is connected in a stable manner with a recursive learning of rule consequent functions possessing linear parameters. The paper is concluded with an evaluation of the proposed algorithm on high-dimensional measurement data for engine test benches.