Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks

• Autorzy: Lei Y. , Zhao D. , Cai H.

Identyfikatory

DOI

10.1515/arsa-2016-0013

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

pole coordinates; prediction; empirical mode decomposition; EMD; neural networks; NN

• Wydawca: Centrum Badań Kosmicznych PAN ; De Gruyter
• Czasopismo: Artificial Satellites : Journal of Planetary Geodesy
• Rocznik: 2016
• Tom: Vol. 51, No. 4
• Strony: 149--161
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Lei Y.

• National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China
• Key Laboratory of Time and Frequency Primary Standards, Chinese Academy of Sciences, Xi'an 710600, China
• University of Chinese Academy of Sciences, Beijing 100049, China

autor

Zhao D.

• National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China
• University of Chinese Academy of Sciences, Beijing 100049, China

autor

Cai H.

• National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China
• Key Laboratory of Time and Frequency Primary Standards, Chinese Academy of Sciences, Xi'an 710600, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).