基于SVM和神经网络组合预测模型物流需求预测  

Research on Logistic Demand Forecast Based on Support Vector Machines and Neural Network

作　　者：田丽[1] 曹安照[1] 王蒙[1] 周明龙[1] 王静[1] 

机构地区：[1]安徽工程大学电气工程学院,安徽芜湖241000

出　　处：《重庆工商大学学报（自然科学版）》2012年第9期61-64,69,共5页Journal of Chongqing Technology and Business University:Natural Science Edition

基　　金：国家自然科学基金(71171002);安徽省自然科学基金(11040606M24)

摘　　要：物流需求的定量数据是区域物流发展政策和规划的重要依据,影响物流需求的因素很多,传统的预测方法无法全面考虑各种因素,预测精度较低。为了提高物流需求预测的精度,采用组合预测的方法,建立一种基于支持向量机和神经网络的组合模型。首先采用支持向量机进行预测得到预测基本数据,然后通过BP神经网络进行残差修正,通过算例仿真分析,结果表明组合预测模型具有更高的精度,是一种有效的预测方法,为物流需求预测提供了新的思路。The quantitative data of logistic demand are the important basis for regional logistic development policy and planning,there are many factors influencing logistic demand,so traditional forecast method can not overall consider all kinds of factors and has lower forecast accuracy.In order to improve the forecast accuracy of logistic demand,combined forecast method is used to set up the combined forecast model based on support vector machines and neural network,firstly support vector machines are used to forecast and forecast basic data are obtained,then residual modification is conducted by BP neural network,and numerical example simulation analysis indicates that the combined forecast model has higher accuracy,is a kind of effective forecast model and provides a new idea for logistic demand forecast.

关 键 词：物流需求 支持向量机 神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]