By Topic

Systems identification using recurrent asymptotically stable neural networks

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

2 Author(s)
Jubien, C.M. ; Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada ; Dimopoulos, N.J.

A training procedure for a class of neural networks that are asymptotically stable is presented. The training procedure is a gradient method which adapts the interconnection weights as well as the relaxation constants and the slopes of the activation functions used so as to minimize the error between the expected and obtained responses. A method for assuring that stability is maintained throughout the training procedure is also given. Such a network was used to identify the dynamic behavior of several nonlinear dynamical systems, a PUMA 560 robot and a boat based on collected rudder/heading data

Published in:

Communications, Computers and Signal Processing, 1993., IEEE Pacific Rim Conference on  (Volume:2 )

Date of Conference:

19-21 May 1993