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Titolo
Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules
Autor(es)
Soggetto
Computer Science
Fecha de publicación
2012
Editore
Springer Science + Business Media
Citación
Intelligent Data Engineering and Automated Learning - IDEAL 2012 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 7435, pp. 842-849.
Abstract
This 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.
URI
ISBN
978-3-642-32638-7 (Print) / 978-3-642-32639-4 (Online)
ISSN
0302-9743 (Print) / 1611-3349 (Online)
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