By Topic

A new approach to weighted fuzzy production rule extraction from neural networks

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)
Tie-Gang Fan ; Machine Learning Center, Hebei Univ., Baoding, China ; Xi-Zhao Wang

There are many advantages of artificial neural networks such as high prediction accuracy, robustness, no requirements on data distribution, but knowledge captured by neural networks is not transparent to users. This results in a major problem for users of neural network-based systems. It is significant to extract rules from neural networks. This paper proposes a new method for extracting weighted fuzzy production rules from trained neural networks by structural learning based on matrix of importance index.

Published in:

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on  (Volume:6 )

Date of Conference:

26-29 Aug. 2004