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dc.contributor.authorSun, Y.
dc.contributor.authorHe, Y.
dc.contributor.authorTan, Y.
dc.date.accessioned2009-06-11T14:22:12Z
dc.date.available2009-06-11T14:22:12Z
dc.date.issued2004
dc.identifier.citationSun , Y , He , Y & Tan , Y 2004 , ' Wavelet neural network approach for fault diagnosis of analogue circuits ' , IEE Proceedings Circuits Devices and Systems , vol. 151 , no. 4 , pp. 379-384 .
dc.identifier.issn1350-2409
dc.identifier.otherPURE: 110724
dc.identifier.otherPURE UUID: 578c967b-f113-41a3-b217-1caa8aebb203
dc.identifier.otherdspace: 2299/3576
dc.identifier.otherScopus: 5044243210
dc.identifier.urihttp://hdl.handle.net/2299/3576
dc.description"The copy of record is available at IET Digital Library." Institution of Engineering and Technology Copyright. [Full text of this article is not available in the UHRA]
dc.description.abstractA systematic method for fault diagnosis of analogue circuits based on the combination of neural networks and wavelet transforms is presented. Using wavelet decomposition as a tool for removing noise from the sampled signals, optimal feature information is extracted by wavelet noise removal, multi-resolution decomposition, PCA (principal component analysis) and data normalisation. The features are applied to the proposed wavelet neural network and the fault patterns are classified. Diagnosis principles and procedures are described. The reliability of the method and comparison with other methods are shown by two active filter examples.en
dc.language.isoeng
dc.relation.ispartofIEE Proceedings Circuits Devices and Systems
dc.titleWavelet neural network approach for fault diagnosis of analogue circuitsen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Engineering and Technology
dcterms.dateAccepted2004
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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