Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks-Reference-Cited by-同舟云学术

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

Published:2016-12-01 Issue:4 Volume:51 Page:149-161
ISSN:2083-6104
Container-title:Artificial Satellites
language:en
Short-container-title:

Author:

Lei Yu¹²³,Zhao Danning¹³,Cai Hongbing¹²

Affiliation:

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

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

3. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract

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.

Publisher

Walter de Gruyter GmbH

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