摘要
为了提高船舶交通客流特征预测的时效性,设计基于大数据相关技术信息,提出将粗糙集和支持向量机预测机制结合的预测分析模型。首先运用粗糙集属性,对大数据下的船舶交通客流信息,进行数据出行约简,删除数据中冗余属性,继而建立支持向量机回归预测机制,将约简后的船舶交通数据样本,作为数据预处理器,通过对条件值进行筛选,并量化为一张二维表格,作为决策表,重新组合成为训练数据样本,输入SVM中,进行学习训练,实现交通客流特征的组合预测。仿真实验表明,该模型预测结果特征比真实性提高29%,有效时序性提高35%,可以证明该预测模型的预测结果时效性更强。
in order to improve the timeliness of prediction of ship traffic passenger flow characteristics,a prediction analysis model combining rough set and support vector machine(SVM)prediction mechanism was proposed based on big data related technical information.,first of all,using rough set attribute for large vessel traffic passenger flow information data,data reduction,travel by reduction algorithm,delete the redundant attributes in the data,and then set up the mechanism of support vector machine(SVM)regression prediction,after reduction of vessel traffic data samples,as the data preprocessor,filtered through the condition value,and quantify as a two-dimensional table,as a decision table,reassembled as training data sample,input of SVM,learning training,to realize traffic flow characteristics of combination forecast.Simulation results show that the characteristics of the predicted results of the model are 29%higher than the authenticity,and the effective timing is 35%higher,which can prove that the predicted results of the prediction model are more time-efficient.
作者
李宇航
LI Yu-hang(Chongqing Vocational College of Construction Engineering,Chongqing 400072,China)
出处
《舰船科学技术》
北大核心
2019年第8期88-90,共3页
Ship Science and Technology
关键词
船舶交通
组合预测
回归机制
约简算法
ship traffic
portfolio forecasting
regression mechanism
reduction algorithm
