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dc.contributor.authorHe, Y.
dc.contributor.authorTan, Y.
dc.contributor.authorSun, Y.
dc.date.accessioned2012-04-16T08:57:54Z
dc.date.available2012-04-16T08:57:54Z
dc.date.issued2003
dc.identifier.citationHe , Y , Tan , Y & Sun , Y 2003 , Class-based neural network method for fault location of large-scale analogue circuits . in Procs of the 2003 Int Symposium on Circuits and Systems : ISCAS '03 . vol. 5 , IEEE , pp. 733-736 . https://doi.org/10.1109/ISCAS.2003.1206417
dc.identifier.otherPURE: 782759
dc.identifier.otherPURE UUID: 47ab2fd7-0d4f-4169-ae90-b7c058010e9b
dc.identifier.otherdspace: 2299/4756
dc.identifier.otherScopus: 0037743757
dc.identifier.urihttp://hdl.handle.net/2299/8317
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dc.description.abstractA new method for fault diagnosis of large-scale analogue circuits based on the class concept is developed in this paper. A large analogue circuit is decomposed into blocks/sub-circuits and the nodes between the blocks are classified into three classes. Only those sub-circuits related to the faulty class need to be treated. Node classification reduces the scope of search for faults, thus reduced after-test time. The proposed method is more suitable for real-time testing and can deal with both hard and soft faults. Tolerance effects are taken into account in the method. The class-based fault diagnosis principle and neural network based method are described in some details. Two non-trivial circuit examples are presented, showing that the proposed method is feasible.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs of the 2003 Int Symposium on Circuits and Systems
dc.rightsOpen
dc.titleClass-based neural network method for fault location of large-scale analogue circuitsen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionScience & Technology Research Institute
dc.relation.schoolSchool of Engineering and Technology
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2003
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1109/ISCAS.2003.1206417
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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