DocumentCode :
669112
Title :
Research on the capacity early warning of oil tanker based on PCA-BP Neural Network method
Author :
Xu Cunyi ; Ding Tao ; Sheng Qing
Author_Institution :
Wuhan Univ. of Technol., Wuhan, China
Volume :
3
fYear :
2013
fDate :
23-24 Nov. 2013
Firstpage :
89
Lastpage :
92
Abstract :
In order to be able to give an accurate assessment on supply-demand lever of Tanker transportation capacity and to reduce the risk of the corresponding market expansion, a model named PCA-BP Neural Network of capacity early warning on oil tanker. Capacity early warning indicators are built from the perspective of supply-demand level, and the model is applied to the simulation and comparative analysis of international oil transportation capacity supply-demand level. In the result, the accuracy of model is 0.0001, and the error is 0.4%-7.3%, which can response the oil tanker capacity supply-demand level well.
Keywords :
backpropagation; fuel storage; neural nets; oil technology; principal component analysis; ships; PCA-BP neural network method; capacity early warning; international oil transportation capacity; market expansion risk reduction; oil tanker; supply-demand level; tanker transportation capacity; Analytical models; Biological neural networks; Indexes; Mathematical model; Principal component analysis; Training; BP neural network; Matlab; Oil transportation; Principal component analysis; Transportation capacity early-warning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Management, Innovation Management and Industrial Engineering (ICIII), 2013 6th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4799-3985-5
Type :
conf
DOI :
10.1109/ICIII.2013.6703673
Filename :
6703673
Link To Document :
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