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Please use this identifier to cite or link to this item: http://hdl.handle.net/10928/973

Title: ニューラルネットワークによる太陽風物理量を用いた地磁気擾乱指数の予測
Other Titles: Prediction for Geomagnetic Disturbances with the Solar Wind Parameters Based on Neural Computational Technique
Authors: 北島, 良三
野和田, 基晴
上村, 龍太郎
Kitajima, Ryozo
Nowada, Motoharu
Kamimura, Ryotaro
Keywords: geomagnetic disturbances
solar wind
space weather
potential learning
neural networks
Issue Date: Dec-2017
Publisher: 成蹊大学理工学部
Abstract: We try to determine what solar wind parameter has an impact on the geomagnetic disturbance based on neural computational method “potential learning (PL)”. In our previous study, using the PL method, the clock angle, which is the angle between the dawn — dusk (By) and north — south (Bz) components of solar wind magnetic field, was extracted as the most significant parameter to disturb the geomagnetic field. However, that result included a little uncertainty due to redundancy of parameters. Therefore, we scrutinize the solar wind parameters to evaluate with the PL method, and retry to extract the parameters to provide an impact on geomagnetic disturbances. We could find that the clock angle, solar wind dynamic pressure and number density impinged on magnetospheric disturbances. These parameters are important in considering geomagnetic disturbances. Our result suggests that the potential learning method is very useful tool to study the solar wind-driven geomagnetic disturbances.
URI: http://hdl.handle.net/10928/973
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