%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Moerland-98.1,
         author = {Moerland, Perry and Fiesler, Emile and Saxena, Indu},
       projects = {Idiap},
          month = {4},
          title = {Discrete All-Positive Multilayer Perceptrons for Optical Implementation},
        journal = {Optical Engineering},
         volume = {37},
         number = {4},
           year = {1998},
           note = {(IDIAP-RR 97-02)},
       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.},
            pdf = {https://publications.idiap.ch/attachments/reports/1997/rr97-02.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/1997/rr97-02.ps.gz},
ipdinar={1997},
ipdmembership={learning},
}