Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter

被引：0

作者：

Kroemer, Pavel ^{[1
,2
]}

Novosad, Tomas ^{[1
]}

Snasel, Vaclav ^{[1
,2
]}

Vera, Vicente ^{[4
]}

Hernando, Beatriz ^{[4
]}

Garcia-Hernandez, Laura ^{[7
]}

Quintian, Hector ^{[3
]}

Corchado, Emilio ^{[2
,3
]}

Redondo, Raquel ^{[5
]}

Sedano, Javier ^{[6
]}

Garcia, Alvaro E. ^{[4
]}

机构：

[1] VSB Tech Univ Ostrava, Dept Comp Sci, Ostrava, Czech Republic

[2] IT4Innovat, Ostrava, Czech Republic

[3] Univ Salamanca, Dept Informat & Automat, Salamanca, Spain

[4] UCM, Fac Odontol, Madrid, Spain

[5] Univ Burgos, Dept Civil Engn, Burgos, Spain

[6] Castilla & Leon Technol Inst, Dept AI & Appl Elect, Burgos, Spain

[7] Univ Cordoba, Area Project Engn, Cordoba, Spain

来源：

SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS | 2013年 / 188卷

关键词：

soft computing; dental milling; prediction; evolutionary algorithms; flexible neural trees; fuzzy rules; industrial applications;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：163 / +

页数：3

共 39 条

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