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

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4233))

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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.

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Author information

Authors and Affiliations

  1. Department of Informatics, Mathematics, Electronics and Transportation (DIMET), University “Mediterranea” of Reggio Calabria, 89100, Reggio Calabria, Italy

    Matteo Cacciola, Antonino Greco, Francesco Carlo Morabito & Mario Versaci

Authors
  1. Matteo Cacciola
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  2. Antonino Greco
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  3. Francesco Carlo Morabito
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  4. Mario Versaci
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Cacciola, M., Greco, A., Morabito, F.C., Versaci, M. (2006). An Exhaustive Employment of Neural Networks to Search the Better Configuration of Magnetic Signals in ITER Machine. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_39

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  • DOI: https://doi.org/10.1007/11893257_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46481-5

  • Online ISBN: 978-3-540-46482-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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