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

Extracting compact T-S fuzzy models using subtractive clustering and particle swarm 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

3 Author(s)
Zhao Liang ; Dept. of Autom., Shanghai JiaoTong Univ., Shanghai ; Yang Yupu ; Zeng Yong

This paper presents a two-stage approach to extract compact Takagi-Sugeno (TS) fuzzy models using subtractive clustering and particle swarm optimization (PSO) from numeric data. On the first stage, the subtractive clustering is employed to partition the input space and extract a fuzzy rules base. On the second stage, the PSO algorithm is used to search the optimal membership functions (MFs), consequent parameters and the rule weights of the crude model obtained on the first stage simultaneously. Simulation results on two benchmark modeling problems show that the proposed approach is effective in finding compact and accurate TS fuzzy models.

Published in:

Control Conference, 2008. CCC 2008. 27th Chinese

Date of Conference:

16-18 July 2008