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

Authors:
Agustín Gajate;Rodolfo E. Haber;José R. Alique;Pastora I. Vega
Affiliations:
Instituto de Automática Industrial, Spanish Council for Scientific Research, Madrid, Spain 28500;Instituto de Automática Industrial, Spanish Council for Scientific Research, Madrid, Spain 28500;Instituto de Automática Industrial, Spanish Council for Scientific Research, Madrid, Spain 28500;Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain 37008
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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Abstract

This 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.