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

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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.

logistics demandleast square method (LSM)multi-layer perceptron (MLP)predictionstrategic planning

WEI Leqin、ZHANG Anguo

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School of Humanities and Teachers' Education, Wuyi University,Wuyishan 354300, China

College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

Educational Research Project of Social Science for Young and Middle Aged Teachers in Fujian Province, ChinaSocial Science Research Project of Education Department of Fujian Province, ChinaKey Project of Education and Teaching Reform of Undergraduate Universities in Fujian Province, China

JAS19371JAS160571FBJG20190130

2020

10.19884/j.1672-5220.202009066

东华大学学报(英文版)
东华大学

东华大学学报(英文版)

影响因子：0.091
ISSN：1672-5220
年,卷(期)：2020.37(6)
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