改进BAS-Elman神经网络的港口货运量预测
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U695.2; TP183

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国家自然科学基金资助项目(71761018, 71462018);


Port freight volume forecast based on improved BAS-Elman Neural Network
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    摘要:

    港口货运量预测对港口的建设发展具有极其重要的意义。为对港口货运量进行科学精准预测,结合天牛须搜索算法(BAS)和蒙特卡洛准则,提出一种改进BAS的Elman神经网络预测模型。收集上海港1989-2018年内的货运量以及当地各项经济数据,建立影响港口货运量预测评估体系,对各项因子进行预处理,消除数据冗余信息对预测的影响。对预处理后的数据进行仿真测试,实验结果表明,该模型预测准确率可达95%以上,有效的提高了港口货运量的预测精度。

    Abstract:

    Port freight volume forecasting is of great significance to the construction and development of ports. To make a scientific and accurate prediction of port freight volume, a modified Elman neural network prediction model was proposed based on the BAS and Monte Carlo criteria. Collect the freight volume of Shanghai Port during 1989-2018 and various local economic data, establish a prediction and evaluation system that affects the freight volume of the port, pre-process various factors, and eliminate the impact of redundant information on the forecast. The simulation test is performed on the pre-processed data. The experimental results show that the prediction the model accuracy can reach more than 95%, which effectively improves the prediction accuracy of port freight volume.

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廖列法,欧阳宗英. 改进BAS-Elman神经网络的港口货运量预测[J]. 科学技术与工程, 2021, 21(7): 2937-2944.
Liao Liefa, Ouyang Zongying. Port freight volume forecast based on improved BAS-Elman Neural Network[J]. Science Technology and Engineering,2021,21(7):2937-2944.

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  • 收稿日期:2020-06-27
  • 最后修改日期:2021-02-15
  • 录用日期:2020-08-10
  • 在线发布日期: 2021-03-31
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