• DocumentCode
    477764
  • Title

    Data Mining Modeling for Electromagnetic Scattering Computing

  • Author

    Chen, Weishi ; Ning, Huansheng ; Mao, Xia ; Wang, Baofa

  • Author_Institution
    Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing
  • Volume
    2
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    137
  • Lastpage
    140
  • Abstract
    Scattering computing of metal-media complex structure and structure with cavity has been one of the problems in electromagnetic (EM) scattering theoretical calculation field for many years. A novel method, substituting data mining modeling for original theoretical modeling, is proposed creatively in this paper, attempting to solve the problem by machine learning theory. Data mining modeling is to construct "EM scattering training model", applying regression analysis algorithm on measurement data, to achieve the effect superior to that theoretical modeling can have. Given an example of regressive estimation of some inlet backscattering RCS curve, both original least square algorithm and support vector regression are used, so an applicable data mining model is established initially for EM scattering computing.
  • Keywords
    data mining; electromagnetic wave propagation; learning (artificial intelligence); least squares approximations; regression analysis; support vector machines; data mining modeling; electromagnetic scattering computing; inlet backscattering RCS curve; least square algorithm; machine learning; metal-media complex structure; regression analysis algorithm; regressive estimation; support vector regression; Data mining; Electromagnetic modeling; Electromagnetic scattering; Machine learning; Machine learning algorithms; Radar cross section; Radar detection; Radar scattering; Radar tracking; Regression analysis; RCS; data mining; least square; support vector regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
  • Conference_Location
    Shandong
  • Print_ISBN
    978-0-7695-3305-6
  • Type

    conf

  • DOI
    10.1109/FSKD.2008.578
  • Filename
    4666095