Research on Port Logistics Demand Forecasting Based on Combination Model of BP-RBF Neural Network
[1]Cai Wanzhen,Huang Han.Research on Port Logistics Demand Forecasting Based on Combination Model of BP-RBF Neural Network[J].Journal of Zhengzhou University (Engineering Science),2019,40(05):84-90.[doi:10.13705/j.issn.1671-6833.2019.02.025]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40卷
Number of periods:
2019 05
Page number:
84-90
Column:
Public date:
2019-10-23
- Title:
- Research on Port Logistics Demand Forecasting Based on Combination Model of BP-RBF Neural Network
- Keywords:
- BP neural network ; RBF neural network ; combined model ; forecast ; port logistic demand
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2019.02.025
- Abstract:
- In order to get the excellent accuracy for port logistic demand forecasting, a combination model based on the BP and RBF neural network was utilized to forecast the logistic demand of Shantou port in this paper. According to the nonlinear change of logistic demand, the BP neural network and RBF neural network were used to establish the single forecasting sub-model separately. And then, the sub-models were combined through the magnitude of the forecasting error to forecast the logistic demand. The simulation was performed by using MATLAB software. Experiment results showed that the combination model could achieve considerably better predictive performances than the single model of BP or RBF neural network. It could reduce the mean absolute percentage error and root mean square error in the logistic demand of Shantou port. These results indi cated that forecast combination could improve the precision of the single neural network model for port logistic demand forecasting, and could help the decision maker in relevant port sector make proper decisions.
- References:
- -
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Last Update:
2019-10-26