• DocumentCode
    2262130
  • Title

    An Improved Neural Network Algorithm and Its Application on Enterprise Strategic Management Performance Measurement Based on Kirkpatrick Model

  • Author

    Li, Tielin ; Yang, Yamei ; Liu, Zhibin

  • Author_Institution
    Econ. & Manage. Coll., Shijiazhuang Railway Inst., Shijiazhuang
  • Volume
    1
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    861
  • Lastpage
    865
  • Abstract
    To evaluate the enterprises´ strategic management performance scientifically and accurately, this paper proposes the improved BP neural network model based on Kirkpatrick model which imports the adjustable activation function and the Levenberg-Marquardt optimization algorithm. The improved model not only can simulate the expert in evaluating the strategic performance and avoiding the subjective mistakes in the evaluation process, but also enhance the learning accuracy and the algorithm convergence speed greatly. The strategic management performance evaluation of 14 enterprises in Hebei Province shows that the improved model is stable and reliable, and this method to evaluate the enterprises´ strategic management performance is feasible.
  • Keywords
    backpropagation; neural nets; optimisation; strategic planning; transfer functions; BP neural network algorithm; Kirkpatrick model; Levenberg-Marquardt optimization algorithm; activation function; enterprise strategic management performance measurement; Artificial neural networks; Cities and towns; Educational institutions; Energy management; Mathematical model; Measurement; Neural networks; Neurons; Power generation economics; Technology management; Enterprise Strategic Management; Kirkpatrick Model; Neural Network Algorithm; Performance Measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3497-8
  • Type

    conf

  • DOI
    10.1109/IITA.2008.168
  • Filename
    4739694