Cart (Loading....) | Create Account
Close category search window

Nonlinear System Control Using a Recurrent Neural Fuzzy Network Based on Reinforcement 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)
Cheng-Jian Lin ; Dept. of CSIE, Nat. Chin-Yi Univ. of Technol., Taiping, Taiwan ; Ying-Ming Lin ; Chi-Yung Lee

This paper proposes a recurrent neural fuzzy network with the reinforcement improved particle swarm optimization (R-IPSO) for solving various control problems. The R-IPSO, which consists of structure learning and parameter learning, is also proposed. The structure learning is adopts several sub-swarms to constitute variable fuzzy systems and uses an elite-based structure strategy (ESS) to find suitable the number of fuzzy rules for solving a problem. The parameter learning is adopts an improved particle swarm optimization (IPSO). The examples have been given to illustrate the performance and effectiveness.

Published in:

Computational Intelligence and Design (ISCID), 2010 International Symposium on  (Volume:2 )

Date of Conference:

29-31 Oct. 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.