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An Exhaustive Employment of Neural Networks to Search the Better Configuration of Magnetic Signals in ITER Machine

  • Matteo Cacciola
  • Antonino Greco
  • Francesco Carlo Morabito
  • Mario Versaci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

Concerning the control of plasma column evolution in ITER machine, the reconstruction of the plasma shape in the vacuum vessel represents an important step. In this work, starting from magnetic measurements, a soft computing approach to estimate the distances of the plasma boundary from the first wall of the vacuum vessel is carried out by means of Neural Networks (NNs). In particular, Multi-Layer Perceptron (MLP) nets have been exploited for the purpose. Finally, to verify the robustness of the proposed approach, any different database and number of input parameters has been used.

Keywords

Root Mean Square Error Magnetic Signal Vacuum Vessel Plasma Boundary Reconstruction Accuracy 
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

  • Matteo Cacciola
    • 1
  • Antonino Greco
    • 1
  • Francesco Carlo Morabito
    • 1
  • Mario Versaci
    • 1
  1. 1.Department of Informatics, Mathematics, Electronics and Transportation (DIMET)University “Mediterranea” of Reggio CalabriaReggio CalabriaItaly

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