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Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules
dc.contributor.authorKrömer, Pavel
dc.contributor.authorNovosád, Tomáš
dc.contributor.authorSnášel, Václav
dc.contributor.authorVera González, Vicente
dc.contributor.authorHernando, Beatriz
dc.contributor.authorGarcía Hernández, Laura
dc.contributor.authorQuintián Pardo, Héctor
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorRedondo Guevara, Raquel
dc.contributor.authorSedano Franco, Javier
dc.contributor.authorGarcía Barbero, Álvaro E.
dc.date.accessioned2017-09-06T09:14:05Z
dc.date.available2017-09-06T09:14:05Z
dc.date.issued2012
dc.identifier.citationIntelligent Data Engineering and Automated Learning - IDEAL 2012 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 7435, pp. 842-849.
dc.identifier.isbn978-3-642-32638-7 (Print) / 978-3-642-32639-4 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/134840
dc.description.abstractThis multidisciplinary study presents the application of two soft computing methods utilizing the artificial evolution of symbolic structures – evolutionary fuzzy rules and flexible neural trees – for the prediction of dental milling time-error, i.e. the error between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titlePrediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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