By Topic

Modeling water treatment process using fuzzy neural network based on subtractive clustering

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)
Wang Li ; College of Automation, Nanjing University of Technology, 210009, China ; Shen Jie

Because of nonlinear, time-varying and time-delaying property, itpsilas difficult to model water treatment process by traditional method, so a Takagi-Sugeno fuzzy model based on subtractive clustering algorithm is proposed in this paper. Firstly, subtractive clustering is used to partition the input space and to determine the initial values of premise parameters and fuzzy rules. Moreover, an improved hybrid study algorithm consisting of a back propagation algorithm and least square algorithm is implemented to optimize the parameters. Finally, this proposed method is used to model the water treatment process, and the simulation results show that it offers the advantages of high precision, fast convergence and fast computing speed.

Published in:

2008 27th Chinese Control Conference

Date of Conference:

16-18 July 2008