By Topic

Study of prediction model on grey relational BP neural network based on rough set

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
$31 $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)
Yun Zhang ; Coll. of Biosystems Eng. & Food Sci., Zhejiang Univ., Hangzhou, China ; Yong He

Artificial neural network is a type of large-scale nonlinear dynamical system capable of recognizing the obscure relationships between diverse variables. Its redundant input nodes often POST http://www.icmlc.org/Author/Author_Rts. With the introduction of rough set and grey relation theories, condition attributes were considered as correlation sequences and decision attributes as reference sequences. And the grey correlation coefficient represented the weight upon which the condition attributes were reduced and the initial decision table was renewed with the remaining core factors. As a result of training the BP neural network by the reduced condition attributes, the prediction precision was improved prominently. In the application of this model to forecast the grain yields of China in 2001 and 2002, the results show great improvement of prediction precision as 0.83% and 1.93% respectively. And the fitting precision of the grain yields in the other 11 years (1990-2000) are all above 99%. The redundancy elimination also increases the network training rate by reducing the input and hidden nodes.

Published in:

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on  (Volume:8 )

Date of Conference:

18-21 Aug. 2005