By Topic

Fuzzy membership function optimization for system identification using an extended Kalman filter

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)
Kosanam, S. ; Dept. of Electr. Eng., Cleveland State Univ., OH ; Simon, D.

The generation of membership functions for fuzzy systems is a challenging problem. In this paper, we use an extended Kalman filter to optimize the membership functions for system modeling, or system identification. We describe the algorithm and then show the result as sub-optimal novel method of system identification. The ideas described in this paper are illustrated for system identification of a nonlinear dynamic system of a permanent magnet synchronous motor. The other interesting observation made is that the proposed system acts as a noise-reducing filter. We demonstrate that the extended Kalman filter can be an effective tool for identifying the parameters of a fuzzy system model

Published in:

Fuzzy Information Processing Society, 2006. NAFIPS 2006. Annual meeting of the North American

Date of Conference:

3-6 June 2006