Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks
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References
- 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.
Language: English
Page range: 149 - 161
Submitted on: Jun 22, 2016
Accepted on: Nov 7, 2016
Published on: Dec 23, 2016
Published by: Polish Academy of Sciences, Space Research Centre
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
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© 2016 Yu Lei, Danning Zhao, Hongbing Cai, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.