Forecasting methods and application of regional logistics demand based on wavelet neural network

Author

Ming, Sun Jian ; Jing, Wu

Author_Institution

Sch. of Comput. & Inf. Eng., Harbin Univ. of Commerce, Harbin, China

Volume

2

fYear

2010

fDate

9-10 Jan. 2010

Firstpage

922

Lastpage

926

Abstract

Wavelet neural network is a neural network combining the wavelet theory with neural network theory, which avoids nonlinear optimization problems such as blindness of the design of BP neural network structure and local optimum, greatly simplifying the training. The use of wavelet neural networks to forecast regional logistics demand provided an important reference for regional logistics systematic planning and the rational allocation of logistic resources. Therefore, the use of regional economic indicators to forecast regional logistics demand had strong feasibility and promotes the coordinated development between regional logistics industry and regional economy. The model reveals nonlinear mapping relationship between the regional economy and regional logistics demand and provides a new idea and method for the regional logistics demand forecasting.

Keywords

backpropagation; forecasting theory; logistics; neural nets; optimisation; wavelet transforms; BP neural network; forecasting methods; neural network theory; nonlinear mapping; nonlinear optimization problems; regional logistics demand; wavelet neural network; wavelet theory; Blindness; Demand forecasting; Design optimization; Economic forecasting; Economic indicators; Industrial economics; Industrial relations; Logistics; Neural networks; Resource management; Forecasting Method; Regional Economy; Regional Logistics Demand; Wavelet Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Logistics Systems and Intelligent Management, 2010 International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4244-7331-1

Type

conf

DOI

10.1109/ICLSIM.2010.5461046

Filename

5461046