• DocumentCode
    3018129
  • Title

    Application of Improved Method Combined Elman NN with SVM in Tender Offer

  • Author

    Jing-min, Wang ; Xin-heng, Liang ; Zou, Yu

  • Author_Institution
    Econ. & Manage. Dept., North China Electr. Power Univ., Baoding, China
  • fYear
    2010
  • fDate
    25-27 June 2010
  • Firstpage
    463
  • Lastpage
    466
  • Abstract
    Traditional method of tender offer is subjective and arbitrary, and the ARIMA Accuracy can´t satisfy the tenderer. We have combined the Elman NN with the SVM model to establish a new hybrid optimization algorithm, which are presented to the bidding tender offer in a project. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature. we can draw the conclusion from the comparison between ENN and SVM network forecast that accuracy of the model is much higher than ARIMA.
  • Keywords
    financial management; neural nets; optimisation; support vector machines; ARIMA accuracy; Elman NN; SVM; hybrid optimization algorithm; tender offer; Artificial neural networks; Biological system modeling; Context modeling; Neurons; Optimization; Power systems; Support vector machines; Elman NN; Optimization algorithm; SVM; Tender offer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Control Engineering (ICECE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-6880-5
  • Type

    conf

  • DOI
    10.1109/iCECE.2010.120
  • Filename
    5631835