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

Dynamic self-organized learning for optimizing the complexity growth of radial basis function neural networks

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

2 Author(s)
Arisariyawong, S. ; Mech. Eng. Dept., Srinakharinwirot Univ., Nakornayok, Thailand ; Charoenseang, Siam

This paper proposes a framework of automatically exploring the optimal size of a radial basis function (RBF) neural network. A dynamic self-organized learning algorithm is presented to adapt the structure of the network. The algorithm generates a new hidden unit based on the steady state error of network and the nearest distance from input data to the center of hidden unit. Furthermore, it also detects and removes any insignificant contributing hidden units. For optimizing the complexity growth of RBF neural network, the growing and pruning are combined during adaptation of RBF neural network structure. The examples of nonlinear dynamical system modeling are presented to illustrate the performance of the proposed algorithm.

Published in:

Industrial Technology, 2002. IEEE ICIT '02. 2002 IEEE International Conference on  (Volume:1 )

Date of Conference:

2002

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.