By Topic

A classifier using fuzzy rules extracted directly from numerical data

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)
Abe, S. ; Hitachi Ltd., Ibaraki, Japan ; Lan, M.-S.

The authors consider the extraction of fuzzy rules directly from numerical data for pattern classification. The fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region for a class, and inhibition hyperboxes which inhibit the existence of data for that class. These rules are extracted from numerical data by recursively resolving overlaps between two classes. Then optimal input variables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks for a licence plate recognition system

Published in:

Fuzzy Systems, 1993., Second IEEE International Conference on

Date of Conference:

1993