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

Autoren Edwin Lughofer
Ulrich Bodenhofer
TitelIncremental learning of fuzzy basis function networks with a modified version of vector quantization
BuchtitelProc. 11th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IMPU 2006)
Typin Konferenzband
VerlagÉditions E.D.K.
BandI
OrtParis, France
AbteilungIDM
ISBN2-84254-112-X
MonatJuly
Jahr2006
Seiten56-63
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.