Results on the Steepness in Backpropagation Neural Networks
Moerland, Perry; Thimm, Georg; Fiesler, Emile
1994

Abstract

The backpropagation algorithm is widely used for training multilayer neural networks. In this publication the steepness of its activation functions is investigated. In specific, it is discussed that changing the steepness of the activation function is equivalent to changing the learning rate and the weights. Some applications of this result to optical and other hardware implementations of neural networks are given.

Details

Title Results on the Steepness in Backpropagation Neural Networks
Author(s) Moerland, Perry ; Thimm, Georg ; Fiesler, Emile
Published in Proceedings of the '94 SIPAR-Workshop on Parallel and Distributed Computing
Editor(s)
Pages 91-94
Conference SI Group for Parallel Systems - Proceedings of the '94 SIPAR-Workshop on Parallel and Distributed Computing
Date 1994
Publisher Institute of Informatics, University P\'erolles, Fribourg, Switzerland
Keywords
Laboratories LIDIAP
Record Appears in Scientific production and competences > STI - School of Engineering > IEM - Institut d'Electricité et de Microtechnique > LIDIAP - L'IDIAP Laboratory
Scientific production and competences > Euler Center for Signal Processing
Conference Papers
Work produced at EPFL
Published
Record creation date 2006-03-10

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