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Prediction of the electron flux environment in geosynchronous orbit using a neural network technique

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Abstract

In this study, a neural network technique is adopted to predict the electron flux in a geosynchronous orbit using several items of solar wind data obtained by ACE spacecraft and magnetic variations observed on the ground as input parameters. Parameter tuning for the back-propagation learning method is attempted for the feed-forward neural network. As a result, the prediction using the combined data of solar wind and ground magnetic data shows a highest prediction efficiency of 0.61, which is enough to adapt to the actual use of the space environment prediction.

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References

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Tokuyama College of Technology, Gakuendai, Shunan, Yamaguchi, 745-8181, Japan

    K. Kitamura & Y. Nakamura

  2. Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Toyohashi, Japan

    M. Tokumitsu & Y. Ishida

  3. Applied Electromagnetic Research Center, National Institute of Information and Communications Technology, Tokyo, Japan

    S. Watari

Authors
  1. K. Kitamura
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  2. Y. Nakamura
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  3. M. Tokumitsu
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  4. Y. Ishida
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  5. S. Watari
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Corresponding author

Correspondence to K. Kitamura.

Additional information

This work was presented in part at the 16th International Symposium on Artificial Life and Robotics, Oita, Japan, January 27–29, 2011

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Kitamura, K., Nakamura, Y., Tokumitsu, M. et al. Prediction of the electron flux environment in geosynchronous orbit using a neural network technique. Artif Life Robotics 16, 389–392 (2011). https://doi.org/10.1007/s10015-011-0957-1

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  • DOI: https://doi.org/10.1007/s10015-011-0957-1

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