Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur

Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Pont, Michael J. and Barrie Jones, N. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur. Pattern recognition letters 23 (5) , pp. 569-577. 10.1016/S0167-8655(01)00133-7

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Abstract

This paper presents a novel technique which may be used to determine an appropriate threshold for interpreting the outputs of a trained radial basis function (RBF) classifier. Results from two experiments demonstrate that this method can be used to improve the performance of RBF classifiers in practical applications.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0167-8655
Last Modified:	23 Oct 2022 13:10
URI:	https://orca.cardiff.ac.uk/id/eprint/109849

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