By Topic

Error estimation of recurrent neural network models trained on a finite set of initial values

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)
Liu, B. ; Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA ; Jennie Si

Addresses the problem of estimating training error bounds of state and output trajectories for a class of recurrent neural networks as models of nonlinear dynamic systems. The bounds are obtained provided that the models have been trained on N trajectories with N independent random initial values which are uniformly distributed over [a, b]m εℛm

Published in:

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on  (Volume:2 )

Date of Conference:

10-12 Dec 1997