By Topic

Principal Component Analysis and Neural Network Ensemble Based Economic Forecasting

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Bangzhu Zhu ; Institute of System Science and Technology, Wuyi University, Jingmen 529020, China; Beijing University of Aeronautics and Astronautics, Beijing 100083, China. E-mail: ; Jian Lin

The application of neural network ensemble (NNE) to economic forecasting can heighten the generalization ability of learning systems through training multiple neural networks and combining their results. In this paper, principal component analysis (PCA) is developed to extract the principal component of the economic data under the prerequisite that the main information of original economic data is not lost, and the input nodes of forecasting model are effectively reduced. Based on Bagging, a NNE constituted by five BP neural networks is employed to forecast GDP of Jiangmen, Guangdong with favorable results obtained, which shows that NNE is superior to simplex neural network, and valid and feasible for economic forecasting.

Published in:

2006 Chinese Control Conference

Date of Conference:

7-11 Aug. 2006