Rare Fault Detection by Possibilistic Reasoning

dc.contributor.authorGoser, Karlde
dc.contributor.authorKanstein, Andreasde
dc.contributor.authorThomas, Marcde
dc.date.accessioned2004-12-07T08:19:45Z
dc.date.available2004-12-07T08:19:45Z
dc.date.created1998de
dc.date.issued1998-11-08de
dc.description.abstractKernel based neural networks with probabilistic reasoning are suitable for many practical applications. But influence of data set sizes let the probabilistic approach fail in case of small data amounts. Possibilistic reasoning avoids this drawback because it is independent of class size. The fundamentals of possibilistic reasoning are derived from a probability/possibility consistency principle that gives regard to relations. It is demonstrated that the concept of possibilistic reasoning is advantageous for the problem of rare fault detection, which is a property desired for semiconductor manufacturing quality control.en
dc.format.extent121988 bytes
dc.format.extent99829 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5354
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15284
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 24de
dc.subject.ddc004de
dc.titleRare Fault Detection by Possibilistic Reasoningen
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

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