| 基于时间序列的港口货物吞吐量GRNN预测模型 |
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| 引用本文： | 周少龙,周锋.基于时间序列的港口货物吞吐量GRNN预测模型[J].上海海事大学学报,2011,32(1):70-73. |
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| 作者姓名： | 周少龙 周锋 |
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| 作者单位： | 武汉理工大学,航运学院,武汉,430063 |
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| 基金项目： | 武汉理工大学自主创新研究基金(444-20510012) |
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| 摘 要： | 为准确进行港口货物吞吐量预测,采用逐步递归的方法,建立基于时间序列的港口货物吞吐量广义回归神经网络(Generalized Regression Neural Network,GRNN)预测模型.利用该模型预测某港口货物吞吐量,结果表明:对于小样本的短期预测,该模型具有适应性好、误差小等特点.
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| 关 键 词： | 港口 货物吞吐量 时间序列 广义回归神经网络 预测模型 |
| 收稿时间： | 7/14/2010 1:28:00 PM |
| 修稿时间： | 2010/11/25 0:00:00 |
GRNN model for prediction of port cargo throughput based on time series |
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| ZHOU Shaolong,ZHOU Feng.GRNN model for prediction of port cargo throughput based on time series[J].Journal of Shanghai Maritime University,2011,32(1):70-73. |
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| Authors: | ZHOU Shaolong ZHOU Feng |
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| Institution: | ZHOU Shaolong,ZHOU Feng((School of Navigation,Wuhan Univ.of Technology,Wuhan 430063,China) |
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| Abstract: | In order to predict the port cargo throughput accurately,the step-by-step recursive method is adopted to establish the Generalized Regression Neural Network(GRNN) model for the prediction of port cargo throughput based on the time series.The model is used to predict the cargo throughput of a port,and the results show that this model is of characteristics of good adaptability and small error,etc,for the short-term prediction of small sample. |
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| Keywords: | port cargo throughput time series generalized regression neural network prediction model |
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