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dc.contributor.authorRibeiro, Bernardete-
dc.identifier.citationIEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews. 35:3 (2005) 401-410en_US
dc.description.abstractSupport vector machines (SVMs) are receiving increased attention in different application domains for which neural networks (NNs) have had a prominent role. However, in quality monitoring little attention has been given to this more recent development encompassing a technique with foundations in statistic learning theory. In this paper, we compare C-SVM and ν-SVM classifiers with radial basis function (RBF) NNs in data sets corresponding to product faults in an industrial environment concerning a plastics injection molding machine. The goal is to monitor in-process data as a means of indicating product quality and to be able to respond quickly to unexpected process disturbances. Our approach based on SVMs exploits the first part of this goal. Model selection which amounts to search in hyperparameter space is performed for study of suitable condition monitoring. In the multiclass problem formulation presented, classification accuracy is reported for both strategies. Experimental results obtained thus far indicate improved generalization with the large margin classifier as well as better performance enhancing the strength and efficacy of the chosen model for the practical case studyen_US
dc.subjectFault detection and diagnosisen_US
dc.subjectKernel learning methodsen_US
dc.subjectModel selectionen_US
dc.subjectRadial basis function (RBF) neural networks (NNS)en_US
dc.subjectSupport vector machines (SVMs)en_US
dc.titleSupport vector machines for quality monitoring in a plastic injection molding processen_US
item.fulltextCom Texto completo-
item.grantfulltextopen- de Ciências e Tecnologia, Universidade de Coimbra- de Coimbra- FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA-
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais
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