Rescaling Of Variables In Back Propagation Learning

Author

A. K. Rigler, Missouri University of Science and Technology
J. M. Irvine
T. P. Vogl

Abstract

Use of the logistic derivative in backward error propagation suggests one source of ill-conditioning to be the decreasing multiplier in the computation of the elements of the gradient at each layer. A compensatory rescaling is suggested, based heuristically upon the expected value of the multiplier. Experimental results demonstrate an order of magnitude improvement in convergence. © 1991.

Recommended Citation

A. K. Rigler et al., "Rescaling Of Variables In Back Propagation Learning," Neural Networks, vol. 4, no. 2, pp. 225 - 229, Elsevier, Jan 1991.

The definitive version is available at https://doi.org/10.1016/0893-6080(91)90006-Q

Department(s)

Mathematics and Statistics

Comments

Office of Naval Research, Grant N00014-88-K-0659

Keywords and Phrases

Backward error propagation; Layered networks; Preconditioning; Rescaling

International Standard Serial Number (ISSN)

0893-6080

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Elsevier, All rights reserved.

Publication Date

01 Jan 1991