Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published June 1991 | Published
Book Section - Chapter Open

Local Training for Radial Basis Function Networks: Towards Solving the Hidden Unit Problem

Abstract

This work examines training methods for radial basis function networks (RBFNs). First, the theoretical and practical motivation for RBFNs is reviewed, as are two currently popular training methods. Next a new training method is developed using well known results from functional analysis. This method trains each kidden unit individually, and is thus called the local training method. The structure of the method allows analysis of individual hidden units; moreover a covariance-related quantity is defined that gives insight into how many hidden units to employ. Two examples illustrate the usefulness of the method. Lastly, an ad hoc method to further improve RBFN performance is demonstrated.

Additional Information

© 1991 IEEE. Tyler Holcomb is a recipient of a National Science Foundation Graduate Fellowship. This research was supported by the Caltech Consortium in Chemistry and Chemical Engineering. Founding members of the Consortium are E. I. du Pont de Nemours and Company, inc., Eastman Kodak Company, Minnesota Mining and Manufacturing Company, and Shell Oil Company Foundation.

Attached Files

Published - 04791821.pdf

Files

04791821.pdf
Files (696.4 kB)
Name Size Download all
md5:e9007ccaafb3f1914537bbb78ff9d7ae
696.4 kB Preview Download

Additional details

Created:
August 20, 2023
Modified:
October 26, 2023