System Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Linguistic fuzzy model identification

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 $31
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)
Hwang, H.-S. ; Dept. of Electr. Eng., Yonsei Univ., Seoul, South Korea ; Woo, K.B.

The paper presents an approach for identifying a fuzzy model composed of fuzzy-logic based linguistic rules for a multi-input/single-output system. The approach includes structure identification and parameter identification. We propose to utilise a fuzzy c-means clustering and genetic algorithm (GA) hybrid scheme to identify the structure and the parameters of a fuzzy model, respectively. To evaluate the advantages and the effectiveness of the suggested approach, we deal with numerical examples. Comparison shows that the proposed approach can produce the fuzzy model with higher accuracy and a smaller number of rules than previously achieved in other works. To show the global optimisation and local convergence of the GA hybrid scheme, we also consider an optimisation problem having a few local minima and maxima

Published in:

Control Theory and Applications, IEE Proceedings -  (Volume:142 ,  Issue: 6 )