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 [BibTeX] [Marc21]
Discrete All-Positive Multilayer Perceptrons for Optical Implementation
Type of publication: Idiap-RR
Citation: Moerland-97.2
Number: Idiap-RR-02-1997
Year: 1997
Institution: IDIAP
Note: Accepted for publication in {\em Optical Engineering}
Abstract: All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examples are the use of non-ideal activation functions which are truncated, asymmetric, and have a non-standard gain, restriction of the network parameters to non-negative values, and the limited accuracy of the weights. In this paper, a backpropagation-based learning rule is presented that compensates for these non-idealities and enables the implementation of all-optical multilayer perceptrons where learning occurs under control of a computer. The good performance of this learning rule, even when using a small number of weight levels, is illustrated by a series of experiments including the non-idealities.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Moerland, Perry
Fiesler, Emile
Saxena, Indu
Added by: [UNK]
Total mark: 0
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