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

  • Agustín Gajate
  • Rodolfo E. Haber
  • José R. Alique
  • Pastora I. Vega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)

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.

Keywords

Transductive reasoning Neuro-fuzzy inference system Modeling Tool wear 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Agustín Gajate
    • 1
  • Rodolfo E. Haber
    • 1
  • José R. Alique
    • 1
  • Pastora I. Vega
    • 2
  1. 1.Instituto de Automática IndustrialSpanish Council for Scientific ResearchMadridSpain
  2. 2.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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