Multivariant Forecasting Mode of Guangdong Province Port throughput with Genetic Algorithms and Back Propagation Neural Network
For better accurate forecasting of port throughput, a back propagation (BP) neural network model with genetic algorithms is proposed. By means of analysis of influence factors for port throughput, the structure of the (BP) neural network model is determined. Then the connection weight matrix of the BP network is designed for chromosomes of genetic algorithms (GA), which is proved to optimize BP network. The port throughput of Guangdong province in China is used for verification, and the result of the experiment shows that GA-BP neural network model has better accuracy, but consumes more time than a traditional BP network model does.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/18770428
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Supplemental Notes:
- © 2013 Fang Feng Ping and Fang Xue Fei
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Authors:
- Ping, Fang Feng
- Fei, Fang Xue
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Conference:
- 13th COTA International Conference of Transportation Professionals (CICTP 2013)
- Location: Shenzhen , China
- Date: 2013-8-13 to 2013-8-16
- Publication Date: 2013-11-6
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 1165-1174
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Serial:
- Procedia - Social and Behavioral Sciences
- Volume: 96
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1877-0428
- Serial URL: http://www.sciencedirect.com/science/journal/18770428/53
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Backpropagation; Forecasting; Genetic algorithms; Neural networks; Port operations; Ports
- Uncontrolled Terms: Throughput (Traffic)
- Geographic Terms: Guangdong Province (China)
- Subject Areas: Marine Transportation; Planning and Forecasting; Terminals and Facilities; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01515039
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 21 2014 3:16PM