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

A new hybrid genetic algorithm and its application to the temperature neural network prediction in TFIH

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

5 Author(s)
Tanggong Chen ; Province-Minist. Joint Key Lab. of Electromagn. Field & Electr. Apparatus Reliability, Hebei Univ. of Technol., Tianjin ; Youhua Wang ; Lingling Pang ; Jingfeng Sun
more authors

Based on the analysis of the characters of genetic algorithm (GA) and particle swarm optimization (PSO), a new hybrid genetic algorithm is presented. This method integrates the well-known GA with PSO by embedding particle swarm operator into GA, and is applied to the temperature neural network (NN) prediction in transverse flux induction heating (TFIH). The results show that the performance of this algorithm is better than that of GA or PSO.

Published in:

Automation Congress, 2008. WAC 2008. World

Date of Conference:

Sept. 28 2008-Oct. 2 2008

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.