| 船舶交通流量预测的灰色神经网络模型 |
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| 引用本文： | 张树奎,肖英杰.船舶交通流量预测的灰色神经网络模型[J].上海海事大学学报,2015,36(1):46-49. |
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| 作者姓名： | 张树奎 肖英杰 |
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| 作者单位： | 1.上海海事大学 商船学院；2.江苏海事职业技术学院 航海技术学院,上海海事大学 商船学院 |
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| 基金项目： | 江苏省教育科学“十二五”规划课题（D201303059） |
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| 摘 要： | 为降低船舶交通流量的预测误差,提高预测精度,在分析传统的灰色模型和反向传播(BackPropagation,BP)神经网络模型优缺点的基础上,构建灰色神经网络模型预测船舶交通流量.以实际测量值作为初始数据构建不同的灰色模型,各种灰色模型的预测值作为神经网络的输入值,得到最佳预测模型.实例分析表明:灰色神经网络模型可提高预测精度,预测结果比较理想,优于单一预测模型;该模型具有所需初始数据少和非线性拟合能力强的特点,用于船舶交通流量预测是可行和有效的.
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| 关 键 词： | 船舶交通量 灰色模型 神经网络 |
| 收稿时间： | 2014/3/26 0:00:00 |
| 修稿时间： | 2014/4/29 0:00:00 |
Grey neural network model for ship traffic flow prediction |
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| Institution: | Navigational Department Jiangsu Maritime Institute |
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| Abstract: | In order to reduce the error and improve the accuracy of ship traffic flow prediction, a Grey neural network model is constructed based on the analysis of the advantages and disadvantages of the traditional Grey model and BackPropagation (BP) neural network model. The real measured data are used as initial data to construct different Grey models. Various prediction results of these models are used as the input of the neural network, and then the optimized prediction model is obtained. A case study shows that the Grey neural network model can improve prediction accuracy, is of good prediction results and better than the single prediction model. The model requires less initial data, is of strong nonlinear fitting ability, and is feasible and effective for the ship traffic flow prediction. |
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| Keywords: | ship traffic flow Grey model neural network |
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