[1]蔡婉贞,黄 翰.基于 BP-RBF神经网络的组合模型预测港口物流需求研究[J].郑州大学学报(工学版),2019,40(05):84-90.
点击复制

基于 BP-RBF神经网络的组合模型预测港口物流需求研究()
分享到:

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年05期
页码:
84-90
栏目:
出版日期:
2019-09-20

文章信息/Info

作者:
蔡婉贞黄 翰
汕头职业技术学院经济管理系,广东汕头;华南理工大学软件学院,广东广州
关键词:
BP神经网络RBF神经网络组合模型预测港口物流需求
Keywords:
BP neural network RBF neural network combined model forecast port logistic demand
文献标志码:
A
摘要:
为了准确、高效地预测港口物流需求量,提出一种基于BP-RBF神经网络的组合预测模型.考虑 到物流需求的非线性变化特点,在建模过程中首先釆用BP与RBF两种神经网络方法分别建立单项预 测子模型,然后依据各子模型预测结果赋予不同权重进一步构建加权组合预测模型.再以汕头港为例, 通过MATLAB软件对港口物流需求量进行仿真预测.结果表明,组合预测模型较单一预测模型具有更高 的预测精度,能有效减少出现较大误差的概率,使预测结果更接近于实际情况,可为港口今后物流发展 规划提供参考.
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 indicated 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.
更新日期/Last Update: 2019-10-26