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

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.