By Topic

A Generalized Regression Neural Network Based on Fuzzy Means Clustering and Its Application in System Identification

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

4 Author(s)
Shi-jun Zhao ; China Univ. of Pet., Dongying ; Jin-lei Zhang ; Xun Li ; Wei Song

A method to simplify the generalized regression neural networks (GRNN) structure with a large numbers of training samples is proposed. The amount of pattern units is proportionate to the training samples. So in order to simplify the GRNN's structure, some of the representative samples should be selected to build the network. This paper takes the fuzzy means clustering algorithm. It combines with a similarity measurement, which is calculated between input elements, to find the best clustering centers. According to the simulation results, this strategy can largely simplify the GRNN's structure and significantly improve the network's efficiency with just a tiny of loss in accuracy. The network structure built in this strategy can learn quickly, and is suitable to deal with the problems of nonlinear system identification.

Published in:

Information Technology Convergence, 2007. ISITC 2007. International Symposium on

Date of Conference:

23-24 Nov. 2007