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

A New Ridgelet Neural Network Training Algorithm Based on Improved 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

6 Author(s)
Rijian Su ; Huazhong Univ. of Sci. & Technol., Wuhan ; Li Kong ; Shengli Song ; Pu Zhang
more authors

An improved particle swarm optimization is used to train ridgelet neural network instead of the traditional gradient algorithms. Firstly, the model of ridgelet neural network and the traditional particle swarm optimization (PSO) algorithm are briefly described. Secondly, an improved particle swarm optimization with self-adaptation mutation factor is proposed. Then the improved particle swarm optimization is applied to rigdelet neural network training. Experimental results demonstrate that the new algorithm is better than the traditional particle swarm optimization algorithm in training ridgelet neural network. It has both a better stability and a steady convergence, and is easy to be realized.

Published in:

Natural Computation, 2007. ICNC 2007. Third International Conference on  (Volume:3 )

Date of Conference:

24-27 Aug. 2007

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.