By Topic

Learning of weighted fuzzy production rules based on fuzzy neural network

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

5 Author(s)
Dong-Mei Huang ; Coll. of Sci., Hebei Agric. Univ., Baoding, China ; Ming-Hu Ha ; Xue-Fei Li ; E. C. C. Tsang
more authors

In this paper, we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to refine or tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs by including local and global weights. By experimenting our method with some existing benchmark examples, the proposed method is found have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is greatly reduced.

Published in:

2005 International Conference on Machine Learning and Cybernetics  (Volume:5 )

Date of Conference:

18-21 Aug. 2005