Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron

魏乐琴; 张安国

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Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron

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摘要

To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.

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《东华大学学报：英文版》 |2020年第6期|526-533|共8页
作者
魏乐琴; 张安国;
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作者单位

School of Humanities and TeachersEducation;

Wuyi University;

Wuyishan 354300;

China;

College of Physics and Information Engineering;

Fuzhou University;

Fuzhou 350108;

China;

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原文格式 PDF
正文语种 chi
中图分类交通工程与交通管理;
关键词
logistics demand; least square method(LSM); multi-layer perceptron(MLP); prediction; strategic planning;

Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron

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