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Application of SVM and PSO Arithmetic in Deep Space Exploration Data Analysis

  • Mingxing ZhouEmail author
  • Jianfeng Zhang
  • Fangyong Lan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 972)

Abstract

A method of SVM optimized by using the PSO arithmetic is presented to solve nonlinear regression estimation problems in deep space exploration data analysis. This method is used to process the microwave brightness temperature (TB) data acquired by the CE-1 satellite. Firstly, the SVM regression model is established and some parameters of which are optimized by using the PSO arithmetic. Then, by training the TB data with the optimized SVM model, the relationship between the TB from four frequency channels and the lunar hour angle is established. Finally, the distribution maps of TB from four frequency channels on the entire lunar surface in certain short period are obtained. The error analysis indicates that the results of this paper can be used in the further study of lunar regolith depth. Furthermore, the abnormal data among the measured data can be found out and modified by using this method.

Keywords

SVM PSO TB data CE-1 Hour angle Data analysis 

References

  1. 1.
    Vapnik, V.: The Nature of Statistical Learning Theory. Information Science and Statistics. Springer, New York (1995).  https://doi.org/10.1007/978-1-4757-3264-1CrossRefzbMATHGoogle Scholar
  2. 2.
    Du, S.-X., Wu, T.-J.: Support vector machines for regression. J. Syst. Simul. 15(11), 1580–1663 (2003)Google Scholar
  3. 3.
    Zheng, Y.C., Bian, W., Su, Y., et al.: Brightness temperature distribution of the moon: result from Chinese Chang’E-1 Lunar Orbiter. In: Goldschmidt Conference 2009, 21–26 June 2009 (2009)Google Scholar
  4. 4.
    Fa, W., Jin, Y.: Analysis of microwave brightness temperature of lunar surface and inversion of regolith layer thickness: primary results of Chang-E 1 multi-channel radiometer observation. Sci. China Ser. F Inf. Sci. 53(1), 168–181 (2010)CrossRefGoogle Scholar
  5. 5.
    Chan, K.L., et al.: Lunar regolith thermal behavior revealed by Chang’E-1 microwave brightness temperature data. Earth Planet. Sci. Lett. 295, 287–291 (2010)CrossRefGoogle Scholar
  6. 6.
    Pedrycz, W., Park, B.J., Pizzi, N.J.: Identifying core sets of discriminatory features using particle swarm optimization. Expert Syst. Appl. 36, 4610–4616 (2009)CrossRefGoogle Scholar
  7. 7.
    Deng, N., Tian, Y.: New Method for Data Mining: Support Vector Machine, pp. 77–78. Science Press, Beijing (2006)Google Scholar
  8. 8.
    Xi, X., Wang, W., Gao, Y.: Fundamentals of Near-Earth Spacecraft Orbit, pp. 20–36. National Defense University Press, Changsha (2003)Google Scholar
  9. 9.
    Zhou, M.X., Zhou, J.J., Wang, F.: Analysis and simulation of microwave brightness temperature on lunar surface. In: 60th International Astronautical Congress (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Panda Electronics Group Co., Ltd.NanjingChina

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