Prediction of the Logistics Demand for Heilongjiang Province Based on Radial Basis Function Algorithm

摘要：

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.

关键词： Prediction Logistics Demand RBF Algorithm BP neural network

作者: Yanhong Chen Shengde Hu Haijun Liu

作者单位: Economy Management College, Northeast Agricultural University, Harbin, 150030, China College of Econ Economy Management College, Northeast Agricultural University, Harbin, 150030, China Food College, Heilongjiang Bayi Agricultural University, Daqing, 163319, China

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2370-2373

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）