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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 , Institute of Electrical and Electronics Engineers (IEEE) , pp. 733-736 . https://doi.org/10.1109/ISCAS.2003.1206417
dc.identifier.otherdspace: 2299/4756
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.format.extent281397
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofProcs of the 2003 Int Symposium on Circuits and Systems
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
rioxxterms.versionofrecord10.1109/ISCAS.2003.1206417
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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