Application of Orthogonal & Iterative Functional Networks to Intermediate and Short-term Clock Error Prediction

  • LIU Qiang ,
  • CHEN Xihong ,
  • HU Denghua ,
  • XUE Lunsheng ,
  • HAN Beibei ,
  • ZHANG Qun
  • 1. Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China;
    2. College of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
    3. Unit No. 93492 of PLA, Beijing 102101, China;
    4. Information & Navigation College, Air Force Engineering University, Xi'an 710077, China

Received date: 2014-05-26

  Revised date: 2014-06-17

  Online published: 2014-09-30


Accurate satellite clock error prediction is of vital importance for satellite stable operation when satellites' clocks are not able to compare with those on the ground. Aiming at the problem of intermediate and short-term clock error prediction, a polynomial model is chosen to predict the clock error, and an orthogonal iterative functional networks algorithm based on a sliding window model is designed, which takes advantage of the non-linear learning ability of functional networks to fit and analyze the clock error model. The analysis shows that when the prediction time is less than 12 h, the predicted errors are between 0.2 ns and 0.5 ns, which is equivalent to IGU P. When the prediction time is 24 h, the overall errors are around 1ns, which is slightly less than IGU P. When the prediction time is a satellite week, the maximum error may reach 130 ns, which does not meet the reqirement of the satellites. It is concluded that the algorithm of the paper is suited for the short-term clock error prediction but not the intermediate and longtime prediction.

Cite this article

LIU Qiang , CHEN Xihong , HU Denghua , XUE Lunsheng , HAN Beibei , ZHANG Qun . Application of Orthogonal & Iterative Functional Networks to Intermediate and Short-term Clock Error Prediction[J]. Science & Technology Review, 2014 , 32(27) : 38 -42 . DOI: 10.3981/j.issn.1000-7857.2014.27.006


[1] 刘强, 孙际哲, 陈西宏, 等. CPSO-LSSVM在自回归钟差预报中的应用分析[J]. 吉林大学学报: 工学版, 2014, 44(3): 807-811. Liu Qiang, Sun Jizhe, Chen Xihong, et al. Application analysis of CPSO-LSSVM algorithm in AR clock error prediction [J]. Journal of Jilin University: Engineering and Technology Edition, 2014, 44(3): 807-811.
[2] 季利鹏, 徐波, 高有涛. 泛函网络在导航卫星钟差中长期预报中的应用[J]. 天文学报, 2013, 54(2): 176-188. Ji Lipeng, Xu Bo, Gao Youtao. The application of functional network in medium-and long-term prediction of clock error of navigation satellite[J]. Acta Astronomica Sinica, 2013, 54(2): 176-188.
[3] 王颖, 徐波, 杨旭海. 一种利用泛函网络进行导航卫星钟差预报的方法研究[J]. 宇航学报, 2012, 33(10): 1401-1406. Wang Ying, Xu Bo, Yang Xuhai. Research on the navigation satellite clock error prediction using functional network[J]. Journal of Astronautics, 2012, 33(10): 1401-1406.
[4] Xu B, Wang Y, Yang X H. Navigation satellite clock error prediction based on functional network[J]. Neural Processing Letters, 2013, 38(2): 305-320.
[5] Xu B, Wang Y, Yang X H. A hybrid model for navigation satellite clock error prediction[J]. Computational Intelligence, 2013, 465: 307-316.
[6] Martinez F G, Waller P. GNSS clock prediction and integrity[C]// Proceedings of The 22nd European Frequency and Time Forum. France: IEEE, 2009: 1137-1142.
[7] Castillo E. Functional networks[J]. Neural Processing Letters, 1998, 7 (3): 151-159.
[8] Castillo E, Gutiérrez J M. Nonlinear time series modeling and prediction using functional networks. extracting information masked by chaos[J]. Physics Letters A, 1998, 244(1): 71-84.
[9] Castillo E, Gutiérrez J M, Cobo A, et al. A minimax method for learning functional networks[J]. Neural Processing Letters, 2000, 11(1): 39-49.
[10] Castillo E, Hadi A S, Lacruz B. Optimal transformations in multiple linear regression using functional networks[C]. Proceedings of Connectionist Models of Neurons, Learning Processes and Artificial Intelligence, 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13-15, 2001. doi: 10.1007/3-540-45720-8_36.
[11] 周永权, 焦李成. 层次泛函网络整体学习算法[J]. 计算机学报, 2005, 28(8): 1277-1286. Zhou Yongquan, Jiao Licheng. Universal learning algorithm of hierarchical function networks[J]. Chinese Journal of Computers, 2005, 28(8): 1277-1286.
[12] 周永权, 赵斌, 焦李成. 序列泛函网络模型及其学习算法与应用[J]. 计算机学报, 2008, 31(7): 1073-1081. Zhou Yongquan, Zhao Bin, Jiao Licheng. Serial function networks method and learning algorithm with applications[J]. Chinese Journal of Computers, 2008, 31(7): 1073-1081.
[13] Fallani F D V, Rodrigues F A, Costa L D F, et al. Multiple pathways analysis of brain functional networks from eeg signals an application to real data[J]. Brain Topogr, 2011, 23: 344-354.
[14] Castellanos N P, Leyva I, Buldú J M, et al. Principles of recovery from traumatic brain injury: reorganization of functional networks[J]. NeuroImage, 2011, 55: 1189-1199.
[15] 周永权, 何登旭, 焦李成, 等. 层次泛函网络学习算法及其在时间序列分析中的应用[J]. 数据采集与处理, 2006, 21(2): 123-127. Zhou Yongquan, He Dengxu, Jiao Licheng, et al. Learning algorithm of hierarchical functional network and its application in time series analysis[J]. Journal of Data Acquisition & Processing, 2006, 21(2): 123-127.
[16] 周永权, 吕咏梅, 申芸. 正交泛函网络函数逼近理论及算法[J]. 计算机科学, 2009, 36(1): 138-141. Zhou Yongquan, Lu Yongmei, Shen Yun. Orthogonal function network approximate theory and learning algorithm[J]. Computer Science, 2009, 36(1): 138-141.