By Topic

Improved learning of fuzzy models by structured optimization

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)
Vachkov, G. ; Dept. of Micro Syst. Eng., Nagoya Univ., Japan ; Fukuda, T.

A special procedure for learning the parameters of Takagi-Sugeno (TS) fuzzy models is proposed in this paper. It is a kind of structured optimization where the antecedent and the consequence parameters are divided into two groups and learned by two separate algorithms. A classical optimization algorithm (random walk with a variable step size) is used for learning the antecedent parameters and a special algorithm for local learning by the least squares method (LSM) is used for identifying the consequence parameters. Two different modifications of this structured optimization scheme are proposed and investigated. Experimentally, it has been shown that the procedure of dividing the whole set of parameters into two subsets being optimized in a multiply loop sequence speeds-up the total learning process. Finally a decomposition principle for reducing the dimensionality of the multi-input fuzzy models is also proposed and investigated on test examples. The proposed methods and algorithms lead to a faster learning and/or faster calculation of the fuzzy models which can be further used for different simulation and control purposes

Published in:

Industrial Electronics, 1999. ISIE '99. Proceedings of the IEEE International Symposium on  (Volume:3 )

Date of Conference: