Analysis and Forecasting of Geomagnetic Field Signal in Active Period

DOI

10.2991/icadme-15.2015.87How to use a DOI?

Keywords

geomagnetic variation field; active period; artificial neural network; modeling and forecasting.

Abstract

Geomagnetic variation is divided into quiet period and active period, while active period is non-periodic and random. This paper utilizes analyze the magnetic variation field signals in active period in time-frequency domain, then models and forecasts the signals by using Artificial Neural Networks. The results show that the 4-hour-forecasting error of the three methods is . In multi-step forecasting, the method of GRNN is smooth, while LNN causes error increasing apparently. RBFNN has the best performance as its MAE is the smallest one for each time.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dingxin Chen
AU  - Daizhi Liu
AU  - Liang Meng
AU  - Yihong Li
AU  - Chao Niu
PY  - 2015/10
DA  - 2015/10
TI  - Analysis and Forecasting of Geomagnetic Field Signal in Active Period
BT  - Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering
PB  - Atlantis Press
SP  - 433
EP  - 437
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-15.2015.87
DO  - 10.2991/icadme-15.2015.87
ID  - Chen2015/10
ER  -