Powiadomienia systemowe
- Sesja wygasła!
Tytuł artykułu
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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
Rocznik
Tom
Strony
149--161
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
- 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
- National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China
- University of Chinese Academy of Sciences, Beijing 100049, China
autor
- 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
- Chen, S., Cowan, C.F.N. and Grant, P.M. (1991). Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks, Vol. 2, No. 2, 302-309.
- Flandrin, P., Rilling, G. and Goncalvés, P. (2004). Empirical Mode Decomposition as a Filter Bank, IEEE SIGNAL PROCESSING LETTERS, Vol. 11, No. 2, 112-114.
- Gambis, D. and Luzum, B. (2011). Earth Rotation Monitoring, UT1 Determination and Prediction. Metrologia, Vol. 48, No. 4, 165-170.
- Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C. and Liu, H.H. (1998). The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society London: A, Vol. 454, 903-995.
- Huang, N.E., Wu, M.L., Qu, W., Long, S.R. and Shen, S.S.P.H. (2003). Application of Hilbert-huang Transform to Non-Stationary Financial Time Series Analysis. Applied Stochastic Models in Business and Industry, Vol. 19, No. 3, 245-268.
- Kalarus, M., Kosek, W., Schuh, H. (2008). Summary of the Earth Orientation Parameters Prediction Comparison Campaign. EGU General Assembly 2008, EGU abstract: EGU2008-A-00595.
- Kalarus, M., Schuh, H., Kosek, W., Akyilmaz, O. and Bizouard, Ch. (2010). Achievements of the Earth Orientation Parameters Prediction Comparison Campaign. Journal of Geodesy, Vol. 84, No. 10, 587-596.
- Kosek, W. (2011). Future Improvements in EOP Prediction. Geodesy for Plant Earth, International Association of Geodesy Symposia, Vol. 136, 513-520.
- Kosek, W., Kalarus, M., Johnson, T.J., Wooden, W.H., McCarthy, D.D. and Popiński, W. (2005). A Comparison of LOD and UT1-UTC Forecasts by Different Combined Prediction Techniques. Artificial Satellites, Vol. 40, No. 2, 119-125.
- Kurkova, V. (1992). Kolmogorov's Theorem and Multilayer Neural Networks. Neural Networks, Vol. 5, No. 3, 501-506.
- Lei, Y., Zhao, D.N. and Cai, H.B. (2015). Extreme Learning Machines for the Predictions of Length of Day. Artificial Satellites, Vol. 50, No. 1, 19-33.
- Liao, D.C., Wang, Q.J., Zhou, Y.H., Liao, X.H. and Huang, C. L. (2012). Long-term Prediction of the Earth Orientation Parameters by the Artificial Neural Network Technique. Journal of Geodynamics, Vol. 62, No. 8, 87-92.
- Park, J. and Sandberg, I.W. (1991). Universal Approximation Using Radial-Basis-Function Networks. Neural Computing, Vol. 3, No. 2, 246-257.
- Schuh, H., Ulrich, M., Egger, D., Müller, J. and Schwegmann, W. (2002). Prediction of Earth Orientation Parameters by Artificial Neural Networks. Journal of Geodesy, Vol. 76, No. 5, 247-258.
- Wang, Q.J., Du, Y.N. and Liu, J. (2014). Introducing Atmospheric Angular Momentum into Prediction of Length of Day Change by Generalized Regression Neural Network Model. Journal of Central South University, Vol. 21, No. 4, 1396-1401.
- Wang, Q.J., Liao, D.C. and Zhou, Y.H. (2008). Real-Time Rapid Prediction of Variations of Earth’s Rotational Rate. Chinese Science Bulletin, Vol. 53, No. 7, 969-973.
- Xu, X.Q., Zotov, L. and Zhou, Y.H. (2012). Combined Prediction of Earth Orientation Parameters. China Satellite Navigation Conference (CSNC) 2012 Proceedings Lecture Notes in Electrical Engineering, Vol. 160, No. 2, 361-369.
- Zhang, X.H., Wang, Q.J., Zhu, J.J. and Zhang, H. (2012). Application of General Regression Neural Network to the Prediction of LOD Change. Chinese Astronomy and Astrophysics, Vol. 36, No. 1, 86-96.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8597539b-50be-4da5-8a6f-4f0246084874