By Topic

Linguistic modeling by hierarchical systems of linguistic rules

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

3 Author(s)
O. Cordon ; Dept. de Ciencias de la Comput. e Inteligencia Artificial, Granada Univ., Spain ; F. Herrera ; I. Zwir

In this paper, we propose an approach to design linguistic models which are accurate to a high degree and may be suitably interpreted. This approach is based on the development of a hierarchical system of linguistic rules learning methodology. This methodology has been thought as a refinement of simple linguistic models which, preserving their descriptive power, introduces small changes to increase their accuracy. To do so, we extend the structure of the knowledge base of fuzzy rule base systems in a hierarchical way, in order to make it more flexible. This flexibilization will allow us to have linguistic rules defined over linguistic partitions with different granularity levels, and thus to improve the modeling of those problem subspaces where the former models have bad performance

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:10 ,  Issue: 1 )