• DocumentCode
    498259
  • Title

    The Design and Application of Enterprise Comprehensive Strength Evaluation Model based on Artificial Neural Network

  • Author

    Zheng, Jin ; Zhong, Yubin

  • Author_Institution
    Math. Dept., Zhanjiang Educ. Coll., Zhanjiang, China
  • Volume
    1
  • fYear
    2009
  • fDate
    19-21 May 2009
  • Firstpage
    531
  • Lastpage
    535
  • Abstract
    In this paper, we combine research the "Forbes" Top500 selection criteria and the indexes data of Real Estate Public Companies of China in 2005 and 2006, set up an indicators system, and proposed an enterprisepsilas comprehensive strength evaluation model based on neural network. Then we evaluate the comprehensive strength of Real Estate Public Companies of China in 2007. Finally, we compare the evaluate result with ldquoTop 10 Real Estate Public Companies of Chinardquo issue by Top 10 Comprehensive Strength of Real Estate Public Companies Evaluation Research Group. The compare result show the businesspsilas comprehensive strength evaluation model is effective and has certain value of theoretical and practical application.
  • Keywords
    business data processing; corporate modelling; learning (artificial intelligence); neural nets; Forbes Top500; Real Estate Public Companies of China; Top 10 Comprehensive Strength of Real Estate Public Companies Evaluation Research Group; Top 10 Real Estate Public Companies of China; artificial neural network; enterprise comprehensive strength evaluation model; index data; indicator system; selection criteria; Artificial intelligence; Artificial neural networks; Companies; Educational institutions; Fuzzy neural networks; Fuzzy systems; Intelligent networks; Intelligent systems; Mathematical model; Mathematics; Artificial Neural Network; Enterprise Comprehensive Strength Evaluation Model; Fuzzy Comprehensive Evaluation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-0-7695-3571-5
  • Type

    conf

  • DOI
    10.1109/GCIS.2009.467
  • Filename
    5209054