Adaptive Forecasting of High-Energy Electron Flux at Geostationary Orbit Using ADALINE Neural Network

Authors:
Masahiro Tokumitsu;Yoshiteru Ishida;Shinichi Watari;Kentarou Kitamura
Affiliations:
Department of Electronic and Information Engineering, Toyohashi University of Technology, Toyohashi, Japan 441-8750;Department of Electronic and Information Engineering, Toyohashi University of Technology, Toyohashi, Japan 441-8750;Applied Electromagnetic Research Center, National Institute of Information and Communications Technology, Tokyo, Japan 184-8795;Department of Mechanical and Electrical Engineering, Tokuyama College of Technology, Yamaguchi, Japan 745-8585
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Year:
2009

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Abstract

High-energy electron flux increases in the recovery phase after the space weather events such as a coronal mass ejection. High-energy electrons can penetrate circuits deeply and the penetration could lead to deep dielectric charging. The forecast of high-energy electron flux is vital in providing warning information for spacecraft operations. We investigate an adaptive predictor based on ADALINE neural network. The predictor can forecast the trend of the daily variations in high-energy electrons. The predictor was trained with the dataset of ten years from 1998 to 2008. We obtained the prediction efficiency approximately 0.6 each year except the first learning year 1998. Furthermore, the predictor can adapt to the changes for the satellite's location. Our model succeeded in forecasting the high-energy electron flux 24 hours ahead.