By Topic

A Learning Algorithm of Fuzzy Model Based on Improved Fuzzy Clustering and QR Decomposition

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

2 Author(s)
Hongwei Wang ; School of Electronic and Information Engineering, Dalian University of Technology, Dalian, China 116024. Email: ; Hong Gu

In this paper, we proposed a learning algorithm for fuzzy modeling based on the improved fuzzy clustering method and QR decomposition. The improved fuzzy clustering method is confirmed by using a new objective function, which includes the influence on the input variables and the output variables exerting the input space of fuzzy model. Fuzzy inference matrix acquired from improved fuzzy clustering method is analyzed on the basis of QR decomposition of matrix. According to analyzing the redundancy of the matrix, the structure of fuzzy system is confirmed in the paper. The structure and parameters of fuzzy model are estimated by means of the proposed algorithm. We demonstrate the performance of the proposed algorithm by using the simulating result of the nonlinear system

Published in:

Industrial Electronics and Applications, 2006 1ST IEEE Conference on

Date of Conference:

24-26 May 2006