DocumentCode
553932
Title
Notice of Retraction
Hybrid grey theories and BP algorithm neural network forecast model in logistics park
Author
Wang Zhan-gen
Author_Institution
Mech. Eng., SHANDONG Univ., Jinan, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
139
Lastpage
142
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.
Hybrid grey theories and BP algorithm neural network forecast model(GBPNN) is established based on the season influence factor from the logistics park side and chose the `trainlm´ to practice from a comparison and analysis of three BP algorithm function which named `traingd, trainlm, thaingdx´. The season influence factor which is used as the GBPNN input layer provided a expert evaluation system to assess cargo flow in the logistics park. Then, as the counts of the network input layers and connotative layers are confirmed by trial calculation, so the local extremum is avoided a lot and the forecasting precision is improved. At lase, by using the distribution data in Gai Jiagou Logistics Park and established the Matlab programming to verify the model, a much better forecasting approach effects and a higher forecasting precision are obtained.
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.
Hybrid grey theories and BP algorithm neural network forecast model(GBPNN) is established based on the season influence factor from the logistics park side and chose the `trainlm´ to practice from a comparison and analysis of three BP algorithm function which named `traingd, trainlm, thaingdx´. The season influence factor which is used as the GBPNN input layer provided a expert evaluation system to assess cargo flow in the logistics park. Then, as the counts of the network input layers and connotative layers are confirmed by trial calculation, so the local extremum is avoided a lot and the forecasting precision is improved. At lase, by using the distribution data in Gai Jiagou Logistics Park and established the Matlab programming to verify the model, a much better forecasting approach effects and a higher forecasting precision are obtained.
Keywords
backpropagation; expert systems; forecasting theory; goods distribution; grey systems; logistics; neural nets; BP algorithm; Gai Jiagou logistics park; Matlab programming; cargo flow; expert evaluation system; hybrid grey theories; neural network forecast model; Accuracy; Algorithm design and analysis; Data models; Forecasting; Logistics; Mathematical model; Predictive models; cargo flow forecasting; influence factor; logistics; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6021901
Filename
6021901
Link To Document