By Topic

An unsupervised neural network using a fuzzy learning rule

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

1 Author(s)
Yong Soo Kim ; Dept. of Comput. Eng., Taejon Univ., South Korea

This paper presents a fuzzy neural network which utilizes a similarity measure of the relative distance and a fuzzy learning rule. A fuzzy learning rule consists of a fuzzy membership value, an intra-cluster membership value, and a function of the number of iterations. The proposed fuzzy neural network updates weights of all committed output neurons regardless of winning or losing. The proposed fuzzy neural network is evaluated using the IRIS data set.

Published in:

Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International  (Volume:1 )

Date of Conference:

22-25 Aug. 1999