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

Li, Y; Pont, MJ; Jones, NB

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.

Citation

Li, Y., Pont, M., & 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), 569-577. https://doi.org/10.1016/S0167-8655%2801%2900133-7

• Journal Article Type: Article
• Publication Date: Mar 1, 2002
• Deposit Date: Jul 28, 2015
• Journal: Pattern Recognition Letters
• Print ISSN: 0167-8655
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 5
• Pages: 569-577
• DOI: https://doi.org/10.1016/S0167-8655%2801%2900133-7
• Publisher URL: http://dx.doi.org/10.1016/S0167-8655(01)00133-7
• Related Public URLs: http://www.journals.elsevier.com/pattern-recognition-letters