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Transit Time Estimation by Artificial Neural Networks

  • T. Tambouratzis
  • M. Antonopoulos-Domis
  • M. Marseguerra
  • E. Padovani
Conference paper

Abstract

The use of interactive activation and competition (IAC) and backpropagation (BP) artificial neural networks (ANNs) for transit time estimation has been investigated in this piece of research. Owing to its competitive nature, the IAC ANN has been found able to correctly estimate the current transit time from short records of signals as well as to quickly follow changes in transit time and to detect when the transit time falls outside a predefined expected range. On the other hand, the interactive nature of the IAC ANN allows it to be robust to significant levels of noise and of the global component. A BP ANN has been appended to the IAC ANN, further allowing for the accurate estimation of decimated transit times.

Keywords

Artificial Neural Network Transit Time Local Component Neutron Density Competitive Nature 
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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References

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    M. Antonopoulos, M. Marseguerra, and E. Padovani. On the fast estimation of transit times — application to bwr simulated data. In Proceedings of the SMORN VII (Volume 2), 1995.Google Scholar
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Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • T. Tambouratzis
    • 1
  • M. Antonopoulos-Domis
    • 1
  • M. Marseguerra
    • 2
  • E. Padovani
    • 2
  1. 1.Institute of Nuclear Technology — Radiation ProtectionNCSR ‘Demokritos’Aghia Paraskevi, AthensGreece
  2. 2.Department of Nuclear EngineeringPolytechnic of MilanMilanoItaly

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