Adaptive Neural Network in Logistics Demand Forecasting

被引：1

作者：

Yin Yanling ^{[1
]}

Bu Xuhui ^{[2
]}

Yu Fashan ^{[1
]}

机构：

[1] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo, Peoples R China

[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Adv Control Syst Lab, Beijing, Peoples R China

来源：

INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL 1, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICICTA.2008.73

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Logistics demand forecasting is an important process between Logistics programming and Logistics resource allocation. The neural network algorithm is usually applied to forecasting logistics demand. However it has the problems of slow convergence and local optimization in searching results when the training data is excessive. This paper presents an adaptive neural network algorithm for logistics demand forecasting. The empirical study shows that the adaptive neural network algorithm has faster convergence and higher precision than neural network algorithm.

引用

页码：168 / +

页数：3

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