TY - GEN
T1 - Recurrent Neural Networks based on LSTM for Predicting Geomagnetic Field
AU - Liu, Tong
AU - Wu, Tailin
AU - Wang, Meiling
AU - Fu, Mengyin
AU - Kang, Jiapeng
AU - Zhang, Haoyuan
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/26
Y1 - 2018/11/26
N2 - The predicting accuracy of geomagnetic field is a major factor influencing magnetic anomaly detection, geomagnetic navigation and geomagnetism. The limitations of current methods consist of complex model, a large number of parameters, method of solving parameters with high complexity and low forecast accuracy during geomagnetic disturbed days. In this paper we explore a deep learning method for forecasting geomagnetic field that adopts structure of recurrent neural networks (RNN) based on long-short term memory (LSTM). This method of LSTM RNN includes analyzing the characteristics of geomagnetic field and training the data set of geomagnetic data with simple and robust mathematical model. Compared with current methods, the high-precision prediction of geomagnetic field based on LSTM RNN is achieved during both geomagnetic quiet and disturbed days. Furthermore, it could be found that the average error and maximum error of LSTM RNN are far smaller than those of the other methods.
AB - The predicting accuracy of geomagnetic field is a major factor influencing magnetic anomaly detection, geomagnetic navigation and geomagnetism. The limitations of current methods consist of complex model, a large number of parameters, method of solving parameters with high complexity and low forecast accuracy during geomagnetic disturbed days. In this paper we explore a deep learning method for forecasting geomagnetic field that adopts structure of recurrent neural networks (RNN) based on long-short term memory (LSTM). This method of LSTM RNN includes analyzing the characteristics of geomagnetic field and training the data set of geomagnetic data with simple and robust mathematical model. Compared with current methods, the high-precision prediction of geomagnetic field based on LSTM RNN is achieved during both geomagnetic quiet and disturbed days. Furthermore, it could be found that the average error and maximum error of LSTM RNN are far smaller than those of the other methods.
KW - Geomagnetic Field
KW - High-precision Prediction
KW - Long-Short Term Memory
KW - Recurrent Neural Networks
UR - https://www.scopus.com/pages/publications/85061821594
U2 - 10.1109/ICARES.2018.8547087
DO - 10.1109/ICARES.2018.8547087
M3 - Conference contribution
AN - SCOPUS:85061821594
T3 - ICARES 2018 - Proceedings of the 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology
SP - 56
EP - 60
BT - ICARES 2018 - Proceedings of the 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2018
Y2 - 20 September 2018 through 21 September 2018
ER -