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 Jones, N. Barrie 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
ISSN:	0167-8655
Last Modified:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129149

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