By Topic

On the Kalman filtering method in neural network training and pruning

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

4 Author(s)
Sum, J. ; Dept. of Comput. Sci., Hong Kong Baptist Univ., Kowloon, Hong Kong ; Chi-sing Leung ; Young, G.H. ; Wing-Kay Kan

In the use of the extended Kalman filter approach in training and pruning a feedforward neural network, one usually encounters the problems of how to set the initial condition and how to use the result obtained to prune a neural network. In this paper, some cues on the setting of the initial condition are presented with a simple example illustrated. Then based on three assumptions: 1) the size of training set is large enough; 2) the training is able to converge; and 3) the trained network model is close to the actual one, an elegant equation linking the error sensitivity measure (the saliency) and the result obtained via an extended Kalman filter is devised. The validity of the devised equation is then testified by a simulated example

Published in:

Neural Networks, IEEE Transactions on  (Volume:10 ,  Issue: 1 )