<abstract xmlns="http://www.w3.org/1999/xhtml"><p>It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed <italic>a priori</italic> from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.</p></abstract>

It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.

Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks

National Time Service Center, Chinese Academy of Sciences, Xi’an 710600,

Key Laboratory of Time and Frequency Primary Standards, Chinese Academy of Sciences, Xi’an 710600,

University of Chinese Academy of Sciences, Beijing 100049,

Artificial Satellites

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short...

Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks

Data publikacji: 23 gru 2016

Zakres stron: 149 - 161

Otrzymano: 22 cze 2016

Przyjęty: 07 lis 2016

DOI: https://doi.org/10.1515/arsa-2016-0013

Słowa kluczowe
pole coordinates, prediction, empirical mode decomposition (EMD), neural networks (NN)

© 2016 Artificial Satellites

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks

Data publikacji: 23 gru 2016

Zakres stron: 149 - 161

Otrzymano: 22 cze 2016

Przyjęty: 07 lis 2016

DOI: https://doi.org/10.1515/arsa-2016-0013

Słowa kluczowepole coordinates, prediction, empirical mode decomposition (EMD), neural networks (NN)

© 2016 Artificial Satellites

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
pole coordinates, prediction, empirical mode decomposition (EMD), neural networks (NN)