Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review,        editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name
E-mail
Phone
Title
Content
Verification Code
Turn off MathJax
Article Contents

XU Hailong, QIAO Shubo, LIN Jiale. Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200505
Citation: XU Hailong, QIAO Shubo, LIN Jiale. Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200505

Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm

doi: 10.13203/j.whugis20200505
Funds:

The National Natural Science Foundation of China(42074010).

  • Received Date: 2021-07-12
  • Objectives The polar motion (PM) is an important part of the Earth rotation parameters (ERP). the prediction error of ERP can be effectively reduced by improving the prediction accuracy of PM. Methods Aiming at the complex time variation characteristics of PM, a high-precision prediction method based on the Volterra adaptive algorithm was proposed for the first time, which taken the PM series as chaos. Firstly, the maximum Lyapunov exponent was calculated by using the small data sets method. This analysis proves that the PM has chaotic characteristics. Then two experiments were performed for the second order Volterra adaptive algorithm. Results The results of the experimental results were compared with the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and Bulletin A, the official forecast product of IERS. The results show that the prediction accuracy of this method is higher than that of EOP PCC, and Xp component prediction accuracy is improved significantly, Yp component can be also slightly improved. Compared with Bulletin A, the accuracy of the two forecast results has advantages and disadvantages. Conclusions The example further proves that our method can obtain good forecast results in the short-term polar motion forecast, especially the prediction period is more accurate than that of the small period.
  • [1] Kosek W, Kalarus M. Niedzielski T. Forecasting of the Earth orientation parameters comparison of different algorithms[J]. Nagoya Journal of Medical Science, 2008,69(3-4):133-137.
    [2] Liao Dechun, Wang Qijie, Zhou Yonghong, et al. Long-term Prediction of the Earth Orientation Parameters by the Artificial Neural Network Technique[J]. Journal of Geodynamics, 2012, 62(DEC):87-92
    [3] Sun Zhangzhen, Xu Tianhe.Prediction of Earth Rotation Parameters Based on Improved Weighted Least Squares and Autoregressive Model[J]. Geodesy & Geodynamics, 2012, 3(03):57-64
    [4] Dick W R, Thaller D. IERS Annual Report 2018[M]. Frankfurt am Main:Germany:Verlag des Bundesamts für Kartographie und Geodäsie. 2020:108-111
    [5] Packard N H, Crutchfield J P, Farmer J D, et al. Geometry from a Time Series[J]. Physical Review Letters, 1980, 45(9):712-716
    [6] Takens F. Detecting strange attractors in turbulence[J]. Lecture Notes in Mathematics.1981, 898:366-381
    [7] Kugiumtzis D. State space reconstruction parameters in analysis of chaotic time series——the role of the time window length[J]. Physica D, Atomic:Nonlinear Phenomena, 1996, 95(1):13-28.
    [8] H.S. Kim, R. Eykholt, J.D. Salas. Nonlinear dynamics, delay times, and embedding windows[J]. Physica D:Nonlinear Phenomena, 1999, 127(1):48-60.
    [9] Rosenstein M T, Collins J J, Luca C J D. A Practical Method for Calculating Largest Lyapunov EXponents from Small Data Sets[J]. Physica D, 1993, 65:117-134
    [10] Kalarus M, Schuh R, Kosek R, et al. Achievements of the Earth Orientation Parameters Prediction Comparison Campaign[J]. Journal of Geodesy, 2010, 84(10):587-596
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(240) PDF downloads(5) Cited by()

Related
Proportional views

Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm

doi: 10.13203/j.whugis20200505
Funds:

The National Natural Science Foundation of China(42074010).

Abstract: Objectives The polar motion (PM) is an important part of the Earth rotation parameters (ERP). the prediction error of ERP can be effectively reduced by improving the prediction accuracy of PM. Methods Aiming at the complex time variation characteristics of PM, a high-precision prediction method based on the Volterra adaptive algorithm was proposed for the first time, which taken the PM series as chaos. Firstly, the maximum Lyapunov exponent was calculated by using the small data sets method. This analysis proves that the PM has chaotic characteristics. Then two experiments were performed for the second order Volterra adaptive algorithm. Results The results of the experimental results were compared with the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and Bulletin A, the official forecast product of IERS. The results show that the prediction accuracy of this method is higher than that of EOP PCC, and Xp component prediction accuracy is improved significantly, Yp component can be also slightly improved. Compared with Bulletin A, the accuracy of the two forecast results has advantages and disadvantages. Conclusions The example further proves that our method can obtain good forecast results in the short-term polar motion forecast, especially the prediction period is more accurate than that of the small period.

XU Hailong, QIAO Shubo, LIN Jiale. Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200505
Citation: XU Hailong, QIAO Shubo, LIN Jiale. Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200505
Reference (10)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return