By Topic

Acquisition of fuzzy rules using fuzzy neural networks with forgetting

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

4 Author(s)
Umano, M. ; Dept. of Math. & Inf. Sci., Osaka Prefecture Univ., Japan ; Fukunaka, S. ; Hatono, I. ; Tamura, H.

We acquire fuzzy rules from data using a fuzzy neural network. First, we generate an initial fuzzy neural network of the specified number of fuzzy rules that have fewer good membership functions than generated using a self-organization algorithm by Kohonen. Then, we tune and prune fuzzy rules based on a structural learning algorithm with forgetting by Ishikawa (1996), where the numerals in the consequent part and the center values and widths of membership functions in the antecedent part are tuned and forgotten a little, and thus redundant rules and variables are pruned to acquire simpler, general rules. We apply the method to the iris classification problem, of Fisher (1936) and have a very good result

Published in:

Neural Networks,1997., International Conference on  (Volume:4 )

Date of Conference:

9-12 Jun 1997