• DocumentCode
    1654077
  • Title

    Notice of Retraction
    Model of grey-mean generating function and its application in aviation equipment accident prediction

  • Author

    Gan Xusheng ; Duanmu Jingshun ; Cong Wei

  • Author_Institution
    XiJing Coll., Xi´an, China
  • Volume
    1
  • fYear
    2010
  • Firstpage
    521
  • Lastpage
    525
  • Abstract
    Notice of Retraction

    After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

    We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

    The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

    A hybrid model based on grey model and mean generating function model for aviation equipment accident prediction is proposed. The model organically combines grey GM(1,1) model with mean generating function model. The former is used to reveal the development trend of aviation equipment accident; the latter is used to characterize the fluctuating law of aviation equipment accident. The model not only takes advantage of both strongpoint but also overcomes their shortcomings. The experiment results show that, the model has a simple modeling process and a good prediction to aviation equipment accident, and is a simple and feasible method of aviation equipment accident prediction.
  • Keywords
    air accidents; air safety; grey systems; principal component analysis; aviation equipment accident prediction; grey model; mean generating function model; principal component analysis; Gallium nitride; Gravity; Predictive models; Aviation equipment accident; Grey GM(1,1) model; Mean generating function; Principal component analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Management Science (ICAMS), 2010 IEEE International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-6931-4
  • Type

    conf

  • DOI
    10.1109/ICAMS.2010.5553108
  • Filename
    5553108