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Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/44923
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dc.titleX̄ control chart pattern identification through efficient off-line neural network training
dc.contributor.authorHwarng, H.Brian
dc.contributor.authorHubele, Norma Faris
dc.date.accessioned2013-10-10T04:37:48Z
dc.date.available2013-10-10T04:37:48Z
dc.date.issued1993
dc.identifier.citationHwarng, H.Brian,Hubele, Norma Faris (1993). X̄ control chart pattern identification through efficient off-line neural network training. IIE Transactions (Institute of Industrial Engineers) 25 (3) : 27-40. ScholarBank@NUS Repository.
dc.identifier.issn0740817X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/44923
dc.description.abstractBack-propagation pattern recognizers (BPPR) are proposed to identify unnatural pattern exhibited on Shewhart control charts. In this paper an off-line analysis is performed to investigate the training and learning speed of these BPPRs on simulated x data. The best configuration of the network is further tested to demonstrate the classification capability of the proposed BPPR.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentDECISION SCIENCES
dc.description.sourcetitleIIE Transactions (Institute of Industrial Engineers)
dc.description.volume25
dc.description.issue3
dc.description.page27-40
dc.identifier.isiutNOT_IN_WOS
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