By Topic

Training matrix parameters by Particle Swarm Optimization using a fuzzy neural network for 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
$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)
Shafiabady, N. ; Dept. of Mechatron. Eng. Technol., Azad Univ. Sci. & Res. center, Tehran ; Teshnehlab, M. ; Aliyari Shooredeli, M.

In this article particle swarm optimization that is a population-based method is applied to train the matrix parameters that are standard deviation and centers of radial basis function fuzzy neural network. We have applied least square and recursive least square in training the weights of this fuzzy neural network.There are four sets of data used to examine and prove that particle swarm optimization is a good method for training these complicated matrices as antecedent part parameters.

Published in:

Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on

Date of Conference:

25-28 Nov. 2007