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Optical Engineering

Discrete all-positive multilayer perceptrons for optical implementation
Author(s): Perry D. Moerland; Emile Fiesler; Indu F. Saxena
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Paper Abstract

All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examples are the use of nonideal activation functions, which are truncated, asymmetric, and have a nonstandard gain; restriction of the network parameters to non-negative values, and the limited accuracy of the weights. A backpropagation-based learning rule is presented that compensates for these nonidealities and enables the implementation of all-optical multilayer perceptrons where learning occurs under computer control. The good performance of this learning rule, even when using a small number of weight levels, is illustrated by a series of computer simulations incorporating the nonidealities.

Paper Details

Date Published: 1 April 1998
PDF: 11 pages
Opt. Eng. 37(4) doi: 10.1117/1.601963
Published in: Optical Engineering Volume 37, Issue 4
Show Author Affiliations
Perry D. Moerland, Inst Dalle Molle d'Int. Art. Perceptive (Switzerland)
Emile Fiesler, Physical Optics Corporation (United States)
Indu F. Saxena, Physical Optics Corp. (United States)


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