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Prediction of Human Behaviour Using Artificial Neural Networks

  • Zhicheng Zhang
  • Frédéric Vanderhaegen
  • Patrick Millot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)

Abstract

This paper contributes to the analysis and prediction of deviate intentional behaviour of human operators in Human-Machine Systems using Artificial Neural Networks that take uncertainty into account. Such deviate intentional behaviour is a particular violation, called Barrier Removal. The objective of the paper is to propose a predictive Benefit-Cost-Deficit model that allows a multi-reference, multi-factor and multi-criterion evaluation. Human operator evaluations can be uncertain. The uncertainty of their subjective judgements is therefore integrated into the prediction of the Barrier Removal. The proposed approach is validated on a railway application, and the prediction convergence of the uncertainty-integrating model is demonstrated.

Keywords

Artificial Neural Network Human Operator Prediction Rate Uncertainty Level Train Movement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhicheng Zhang
    • 1
  • Frédéric Vanderhaegen
    • 2
  • Patrick Millot
    • 2
  1. 1.Division of I&C and Electrical SystemsFramatome ANP, Tour ArevaParis La DefenseFrance
  2. 2.CNRS UMR 8530, Laboratoire d’Automatique, de Mécanique et d’Informatique industrielles et HumainesUniversity of Valenciennes, Le Mont HouyValenciennesFrance

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