ARTICLE Moerland-98.1/IDIAP Discrete All-Positive Multilayer Perceptrons for Optical Implementation Moerland, Perry Fiesler, Emile Saxena, Indu EXTERNAL http://publications.idiap.ch/attachments/reports/1997/rr97-02.pdf PUBLIC Optical Engineering 37 4 1305-1315 1998 April 1998 (IDIAP-RR 97-02) 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.