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Título
Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter
dc.contributor.authorKrömer, Pavel
dc.contributor.authorNovosád, Tomáš
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.contributor.authorVáclav, Snášel
dc.date.accessioned2017-09-06T09:17:13Z
dc.date.available2017-09-06T09:17:13Z
dc.date.issued2013
dc.identifier.citationSoft Computing Models in Industrial and Environmental Applications Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing. Volumen 188, pp. 163-172.
dc.identifier.isbn978-3-642-32921-0 (Print) / 978-3-642-32922-7 (Online)
dc.identifier.issn2194-5357(Print)/ 2194-5365(Online)
dc.identifier.urihttp://hdl.handle.net/10366/135168
dc.description.abstractThis multidisciplinary study presents the application of two well known soft computing methods – flexible neural trees, and evolutionary fuzzy rules – for the prediction of the error parameter 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.titleEvaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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