%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 12:57:46 PM

@INPROCEEDINGS{Moerland-96.3,
         author = {Moerland, Perry and Fiesler, Emile and Saxena, Indu},
       keywords = {activation function, liquid crystal light valve (LCLV), non-negative neural networks, optical multilayer perceptron, weight discretization},
       projects = {Idiap},
          title = {Overcoming Inaccuracies in Optical Multilayer Perceptrons},
      booktitle = {Proceedings of the First International Symposium on Neuro-Fuzzy Systems (AT'96)},
           year = {1996},
      publisher = {AATI},
       location = {Lausanne, Switzerland},
       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 use of limited accuracy for the weights. In this paper an adaptation of the backpropagation learning rule is presented that compensates for these three non-idealities. The good performance of this learning rule is illustrated by a series of experiments. This algorithm enables the implementation of all-optical multilayer perceptrons where learning occurs under control of a computer.},
dates={August 29--31},
ipdmembership={learning},
}