Prediction of the logistics demand for Heilongjiang province based on radial basis function algorithm

Author

Yanhong Chen ; Shengde Hu ; Haijun Liu

Author_Institution

Econ. Manage. Coll., Northeast Agric. Univ., Harbin, China

fYear

2012

fDate

23-25 May 2012

Firstpage

2358

Lastpage

2361

Abstract

In order to predict the logistics demand accurately, neural network model based on radial basis function (RBF) algorithm is used. The data of 1991 to 2005 are chosen as training samples. The samples from 2005 to 2008 as input variant are used to test the data from 2006 to 2009. The results shows that the maximal relative error is 2.14% (<;4%). RBF network model through training can predict the logistics demand exactly with better generalization in addition. The results showed that the established neural network model have both satisfying fitting and predicting precision. Conclusions can be drawn that the model is more accurate. It has certain practical value according to the establishment of RBF neural network model for predicting logistics demand.

Keywords

demand forecasting; learning (artificial intelligence); logistics; prediction theory; radial basis function networks; Heilongjiang province; RBF neural network model; logistics demand prediction; maximal relative error; neural network model; precision prediction; radial basis function algorithm; training samples; Biological neural networks; Economics; Educational institutions; Logistics; Prediction algorithms; Predictive models; BP neural network; Logistics Demand; Prediction; RBF Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6244377

Filename

6244377