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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Li, Yuhua and Pont, Michael J (2002) Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where 'unknown' faults may occur. Pattern Recognition Letter, 23 (5). pp. 569-577. [Journal article]

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URL: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V15-4442283-2&_user=126978&_coverDate=03%2F31%2F2002&_rdoc=7&_fmt=summary&_orig=browse&_srch=doc-info(%23toc%235665%232002%23999769994%23284547%23FLA%23display%23Volume)&_cdi=5665&_sort=d&_docanchor=&view=c&_ct=11&_acct=C000010438&_version=1&_urlVersion=0&_userid=126978&md5=2e7d89599a81e33d203177fa868c1f3d

DOI: 10.1016/S0167-8655(01)00133-7

Abstract

This paper develops a technique for determining a reliable threshold for RBF classifiers. A two-phase approach is proposed to RBF classifier use in situations where unknown faults may occur: the first phase deals with the possibility of unknown faults; in the second phase, the classifier threshold is modified through retraining using all available data, including newly collected data about unknown faults. The approach is easy to use and is demonstrated to be particularly effective in classification problems where novelty detection capability is required.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:8639
Deposited By:Dr Yuhua Li
Deposited On:03 Feb 2010 11:28
Last Modified:03 Feb 2010 11:28

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