• DocumentCode
    2768955
  • Title

    Research on the Application of RBF NN on Enterprise Early-warning

  • Author

    Yang, Shuping

  • Author_Institution
    Dept. of Econ. & Manage., Dezhou Univ., Dezhou, China
  • Volume
    2
  • fYear
    2009
  • fDate
    13-15 Nov. 2009
  • Firstpage
    126
  • Lastpage
    129
  • Abstract
    Enterprise early-warning management is a new mode in the modern enterprise management. The introduction of neural network (NN) into enterprise management is one of the newly development orientations of artificial intelligence. The radial basis function (RBF) NN was introduced into enterprise early-warning management. The method of compound index was applied to deal with the indexes. And a dynamic nearest neighbor-clustering algorithm was putted forward to computer the hidden nodes´ number and center value, which overcome the dependence of most neighbor algorithm on parameters. Using the monitoring indexes based on the compound index method, the mathematics model was constructed. Simulation result that adopted RBF NN indicates that the method has good effect, which also proves the validity of this method.
  • Keywords
    artificial intelligence; commerce; pattern clustering; radial basis function networks; artificial intelligence; enterprise early-warning management; nearest neighbor-clustering; neural network; radial basis function; Application software; Clustering algorithms; Conference management; Economic forecasting; Environmental economics; Environmental management; Heuristic algorithms; Neural networks; Research and development management; Technology management; RBF neural network; compound index; dynamic nearest neighbor-clustering algorithm; enterprise early-warning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Technology and Development, 2009. ICCTD '09. International Conference on
  • Conference_Location
    Kota Kinabalu
  • Print_ISBN
    978-0-7695-3892-1
  • Type

    conf

  • DOI
    10.1109/ICCTD.2009.50
  • Filename
    5360121