By Topic

Regularized numerical optimization of fuzzy rule bases

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)
Himmelbauer, J. ; Software Competence Center Hagenberg, Austria ; Drobics, M.

This work is devoted to the mathematical analysis and the numerical solution of data-driven optimization for an important class of fuzzy controllers, so-called Sugeno controllers. In contrast to other approaches which optimize the underlying fuzzy sets, we mainly focus on the linear approximation of the output variable according to the input data. While the first approach leads to nonlinear problems, the latter results in a free, linear least squares system to be solved. Therefore this approach can be used for high dimensional problems as well, when due to the increasing complexity nonlinear systems are no longer applicable. By applying Tikhonov regularization we get stable and fast algorithms that create sufficiently optimized controllers; with saving their interpretability. Finally we show, how variable selection can be used to increase interpretability and to reduce computation time.

Published in:

Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on  (Volume:3 )

Date of Conference:

25-29 July 2004