DocumentCode
3259204
Title
Forecasting model of automobile logistics demand based on Gray Residual-BP neural network
Author
Fang, Jichen ; Gao, Feifei ; Zhang, Qiang ; Zhang, Qin ; Zhan´gen Wang ; Shi, Mengzhu
Author_Institution
Sch. of Mech. Eng., Shandong Univ., Jinan, China
Volume
9
fYear
2010
fDate
16-18 Oct. 2010
Firstpage
4221
Lastpage
4225
Abstract
This paper provides a forecast method based on Gray Residual-Back Propagation neural network (GRPBNN), which predicts automobile logistics demand and accomplishes it by using matlab workbox. The forecast result of the logistics demand of a certain car shows that it matches the real figure. The result forecasted by this method is accurate, and the fitting accuracy is acceptable.
Keywords
automobile industry; backpropagation; logistics; mathematics computing; neural nets; Gray residual-back propagation neural network; automobile logistics demand; forecasting model; matlab workbox; Artificial neural networks; Automobiles; Automotive engineering; Industries; Logistics; Predictive models; Training; Gray Residual-BP Neural Network; automobile logistics demand; forecast;
fLanguage
English
Publisher
ieee
Conference_Titel
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location
Yantai
Print_ISBN
978-1-4244-6513-2
Type
conf
DOI
10.1109/CISP.2010.5646906
Filename
5646906
Link To Document