By Topic

New second-order algorithms for recurrent neural networks based on conjugate gradient

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)
Campolucci, P. ; Dipt. di Elettronica e Autom., Ancona Univ., Italy ; Simonetti, M. ; Uncini, A. ; Piazza, F.

We derive two second-order algorithms, based on the conjugate gradient method, for online training of recurrent neural networks. These algorithms use two different techniques to extract second-order information on the Hessian matrix without calculating or storing it and without making numerical approximations. Several simulation results for nonlinear system identification tests by locally recurrent neural networks are reported for both the off-line and online case

Published in:

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on  (Volume:1 )

Date of Conference:

4-8 May 1998