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Título

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

AutorGajate, Agustín ; Haber Guerra, Rodolfo E. ; Alique López, José Ramón ; Vega, Pastora I.
Palabras claveTransductive reasoning
Neuro-fuzzy inference system
Modeling
Tool wear
Fecha de publicaciónjun-2009
EditorSpringer
CitaciónHAIS 2009, LNAI 5572, pp. 113–120, 2009.
ResumenThis 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.
Versión del editorwww.springer.com/lncs
URIhttp://hdl.handle.net/10261/13771
ISSN0302-9743
Aparece en las colecciones: (IAI) Comunicaciones congresos
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