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
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