• DocumentCode
    3112341
  • Title

    Application of DM in E-Government Based on Combined Grey Neural Network

  • Author

    Qu Zhiming

  • Author_Institution
    Sch. of Civil Eng., Hebei Univ. of Eng. Handan, Handan, China
  • fYear
    2009
  • fDate
    8-9 Dec. 2009
  • Firstpage
    51
  • Lastpage
    54
  • Abstract
    Using grey system, satisfaction mining (DM) technology and radial basis function (RBF) neural network method, the combined model of grey system and RBF neural network is setup, which aims at solving the problems of E-government. The results show that, in short-term prediction, grey system is an effective way and RBF has perfect ability to study. The combined grey neural network (CGNN) has the dual properties of trend and fluctuation under the condition of combining with the time-dependent sequence satisfaction. It is concluded that great improvement comparing with any methods of trend prediction and simple factor in CGNN is stated and described in E-government.
  • Keywords
    government data processing; grey systems; radial basis function networks; E-government; combined grey neural network; grey system; radial basis function neural network method; satisfaction mining technology; time-dependent sequence satisfaction; Civil engineering; Data mining; Decision making; Delta modulation; Electronic government; Fluctuations; Innovation management; Logic; Neural networks; Predictive models; CGNN; DM; E-government; RBF neural network; grey system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovation Management, 2009. ICIM '09. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3911-9
  • Type

    conf

  • DOI
    10.1109/ICIM.2009.19
  • Filename
    5381291