By Topic

Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning

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 $31
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

1 Author(s)
Chen, S. ; Dept. of Electr. & Electron. Eng., Portsmouth Univ., UK

An improved clustering and recursive least squares (RLS) learning algorithm for Gaussian radial basis function (RBF) networks is described for modelling and predicting nonlinear time series. Significant performance gain can be achieved with a much smaller network compared with the usual clustering and RLS method

Published in:

Electronics Letters  (Volume:31 ,  Issue: 2 )