[1]张晓雷,黄洪琼.基于优化的灰色马尔可夫模型对船舶流的预测[J].计算机技术与发展,2018,28(10):101-104.[doi:10.3969/ j. issn.1673-629X.2018.10.021]
　ZHANG Xiao-lei,HUANG Hong-qiong.Prediction of Ship Traffic Flow Based on Optimized Grey Markov Prediction Model[J].,2018,28(10):101-104.[doi:10.3969/ j. issn.1673-629X.2018.10.021]
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基于优化的灰色马尔可夫模型对船舶流的预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年10期
页码:
101-104
栏目:
智能、算法、系统工程
出版日期:
2018-10-10

文章信息/Info

Title:
Prediction of Ship Traffic Flow Based on Optimized Grey Markov Prediction Model
文章编号:
1673-629X(2018)10-0101-04
作者:
张晓雷; 黄洪琼
上海海事大学 信息工程学院,上海 201306
Author(s):
ZHANG Xiao-lei; HUANG Hong-qiong
School of Information Engineering,Shanghai Maritime University,Shanghai 201306,China
关键词:
船舶交通流量; 季节指数; 灰色预测; 马尔可夫模型; 预测
Keywords:
ship traffic flow; seasonal index; gray prediction; Markov model; prediction
分类号:
TP391.9
DOI:
10.3969/ j. issn.1673-629X.2018.10.021
文献标志码:
A
摘要:
为了提高船舶交通流预测的效率和准确率,综合考虑季节、气候等因素,通过分析历史船舶流量数据,在传统的灰色模型基础上构建了基于季节指数的灰色-马尔可夫预测模型。 模型将纵向与横向分析相结合的方法运用到船舶预测中,通过季节指数修正船舶的横向季节性变化,再用灰色模型进行预测,最后通过马尔可夫进行误差修正。 利用武汉大桥断面的船舶流量数据对该模型进行了实例分析,使用 MATLAB 将 BP 神经网络模型、GM(1,1)模型与优化后的灰色马尔可夫模型进行仿真预测,结果表明,Markov-GM(1,1)模型具有更高的预测精度和效率,从而能够相对准确、高效地对船舶交通流量进行预测。
Abstract:
In order to improve the efficiency and accuracy of ship traffic flow forecast,on the basis of the traditional grey model,we build a grey Markov model based on seasonal index by analyzing historical traffic data,comprehensively considering seasons,climates and so on. This model applies a combination of longitudinal and lateral analysis method to ship traffic prediction. Firstly through seasonal index the change of ship transversal seasons is revised. Then the grey model is used for prediction. Finally,the Markov model is applied to cor- rect deviations. The model is verified by the example analysis by ship traffic data of Wuhan bridge section,and BP neural network mod- el,GM(1,1) model and the optimized grey Markov model are simulated and predicted by MATLAB. The results show that Markov- GM(1,1) model has higher prediction accuracy and efficiency,which can predict ship traffic flow accurately and efficiently.
更新日期/Last Update: 2018-10-10