REPORT Moerland-97.2/IDIAP Discrete All-Positive Multilayer Perceptrons for Optical Implementation Moerland, Perry Fiesler, Emile Saxena, Indu EXTERNAL https://publications.idiap.ch/attachments/reports/1997/rr97-02.pdf PUBLIC Idiap-RR-02-1997 1997 IDIAP Accepted for publication in {\em Optical Engineering} 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.