By Topic

An on-line learning algorithm for recurrent neural networks using variational methods

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)
Won-Geun Oh ; Dept. of Comput. & Commun. Eng., Sunchon Nat. Univ., Chonnam, South Korea ; Byung-Suhl Suh

In this paper, an on-line learning algorithm for recurrent neural networks (RNN) using optimal control and a variational method is proposed. First, we obtain optimal weights given by a two-point boundary-value problem using the variational methods. And then the local gradient descent algorithm is derived such that on-line training is possible. This method is intended to be used on learning complex dynamic mappings between time-varying input-output data. Therefore it is useful for nonlinear control, identification, and signal processing applications of RNN. Simulation results for nonlinear plant identification are illustrated

Published in:

Circuits and Systems, 1997. Proceedings of the 40th Midwest Symposium on  (Volume:1 )

Date of Conference:

3-6 Aug 1997