By Topic

Estimation of artificial neural network parameters for nonlinear system identification

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

3 Author(s)
Ruchti, T.L. ; Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA ; Brown, R.H. ; Garside, J.J.

A unified framework for representing ANN (artificial neural network) training algorithms is developed by considering weight selection as a parameter estimation problem. Three existing ANN training strategies are reviewed within this framework, i.e., gradient-descent backpropagation, the extended Kalman algorithm, and the recursive least squares method. A strikingly different approach to error backpropagation is presented, resulting in the development of a novel method of backward signal propagation and target state generation for embedded layers. The proposed technique is suitable for implementation with a linear-Kalman based update algorithm and is applied with a time-varying method of covariance modification for the elimination of transients associated with initial conditions. Results from a nonlinear identification experiment demonstrate an increased rate of convergence in comparison with backpropagation. The new algorithm displayed similar rates of parameter convergence and a decreased computational overhead compared to the extended Kalman algorithm

Published in:

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Date of Conference: