• DocumentCode
    3523854
  • Title

    Spectrum prediction for high-frequency radar based on Extreme Learning Machine

  • Author

    Zhifen Yang ; Ling Yang ; Yanping Fu

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China
  • fYear
    2015
  • fDate
    27-29 March 2015
  • Firstpage
    235
  • Lastpage
    239
  • Abstract
    In this paper, a new predictive method based on Extreme Learning Machine is proposed to predict the spectrum data obtained from by frequency monitoring system of high-frequency radar. In order to improve the forecasting accuracy and real-time of spectrum prediction of high-frequency radar, Empirical Mode Decomposition method is used for the preprocessing of spectrum data. Based on the simulation environment of MATLAB, compared with the predictive method based on support vector regression, the results show that the proposed method performs better both at forecasting accuracy and speed.
  • Keywords
    learning (artificial intelligence); radar computing; radar signal processing; regression analysis; MATLAB; empirical mode decomposition method; extreme learning machine; forecasting accuracy; forecasting speed; frequency monitoring system; high-frequency radar; predictive method; simulation environment; spectrum data; spectrum prediction; support vector regression; Backscatter; Forecasting; IP networks; MATLAB; Nickel; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computational Intelligence (ICACI), 2015 Seventh International Conference on
  • Conference_Location
    Wuyi
  • Print_ISBN
    978-1-4799-7257-9
  • Type

    conf

  • DOI
    10.1109/ICACI.2015.7184784
  • Filename
    7184784