Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering

Analysis and Forecasting of Geomagnetic Field Signal in Active Period

Authors
Dingxin Chen, Daizhi Liu, Liang Meng, Yihong Li, Chao Niu
Corresponding Author
Dingxin Chen
Available Online October 2015.
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/).

Download article (PDF)

Volume Title
Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
October 2015
ISBN
978-94-6252-113-1
ISSN
2352-5401
DOI
10.2991/icadme-15.2015.87How to use a DOI?
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/).

Cite this article

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  -