• DocumentCode
    2284944
  • Title

    Generalized PLS regression forecast modeling of warship equipment maintenance cost

  • Author

    Xie, Li ; Wei, Ru-xiang ; Jiang, Tie-Jun ; Zhang, Ping

  • Author_Institution
    Dept. of Equip. Econ. & Manage., Naval Univ. of Eng., Wuhan, China
  • fYear
    2009
  • fDate
    14-16 Sept. 2009
  • Firstpage
    607
  • Lastpage
    612
  • Abstract
    Aiming at the small sample, more latent variables and the multicollinearity among them in the forecast modeling of warship equipment maintenance cost, a method of improving the generalization ability of PLS was presented, which was on the base of the partial least squares(PLS) regression with shrink-magnifying, and extended the shrinking factor more by shrinking or magnifying the inputs of different sample to different extent, in which the cross training between training set and testing set was implemented. Further more, the foregoing modeling process was applied to the forecast modeling of warship equipment maintenance cost, in which the genetic algorithm was used to seek the best shrinking factor vector. Finally, by comparing with the PLS which distills one, two and three principal components and PLS with shrink-magnifying approach, the method presented in this paper demonstrates the best.
  • Keywords
    costing; least squares approximations; maintenance engineering; military vehicles; regression analysis; ships; foregoing modeling process; partial least squares regression; regression forecast modeling; warship equipment maintenance cost; Conference management; Costs; Economic forecasting; Engineering management; Genetic algorithms; Least squares methods; Management training; Predictive models; Temperature; Testing; PLS; forecast; maintenance cost; shrink-magnifying approach; shrinking factor; small sample; warship equipment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering, 2009. ICMSE 2009. International Conference on
  • Conference_Location
    Moscow
  • Print_ISBN
    978-1-4244-3970-6
  • Electronic_ISBN
    978-1-4244-3971-3
  • Type

    conf

  • DOI
    10.1109/ICMSE.2009.5317378
  • Filename
    5317378