2026-09-16T19:44:17Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1351682025-04-30T20:38:34Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811

Krömer, Pavel Novosád, Tomáš Vera González, Vicente Hernando, Beatriz García Hernández, Laura Quintián Pardo, Héctor Corchado Rodríguez, Emilio Santiago Redondo Guevara, Raquel Sedano Franco, Javier García Barbero, Álvaro E. Václav, Snášel 2017-09-06T09:17:13Z 2017-09-06T09:17:13Z 2013 Soft Computing Models in Industrial and Environmental Applications Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing. Volumen 188, pp. 163-172. 978-3-642-32921-0 (Print) / 978-3-642-32922-7 (Online) 2194-5357(Print)/ 2194-5365(Online) http://hdl.handle.net/10366/135168 This 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. en https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivs 3.0 Unported Computer Science Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter info:eu-repo/semantics/conferenceObject