English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/13771
Share/Impact:
Statistics
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Transductive-Weighted Neuro-fuzzy Inference System for Tool Wear Prediction in a Turning Process

AuthorsGajate, Agustín ; Haber Guerra, Rodolfo E. ; Alique López, José Ramón ; Vega, Pastora I.
KeywordsTransductive reasoning
Neuro-fuzzy inference system
Modeling
Tool wear
Issue DateJun-2009
PublisherSpringer
CitationHAIS 2009, LNAI 5572, pp. 113–120, 2009.
AbstractThis paper presents the application to the modeling of a novel technique of artificial intelligence. Through a transductive learning process, a neuro-fuzzy inference system enables to create a different model for each input to the system at issue. The model was created from a given number of known data with similar features to data input. The sum of these individual models yields greater accuracy to the general model because it takes into account the particularities of each input. To demonstrate the benefits of this kind of modeling, this system is applied to the tool wear modeling for turning process.
Publisher version (URL)www.springer.com/lncs
URIhttp://hdl.handle.net/10261/13771
ISSN0302-9743
Appears in Collections:(IAI) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
Transductive Weighted NeuroFuzzy Inference System.pdf1,44 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.