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 [BibTeX] [Marc21]
Overcoming Inaccuracies in Optical Multilayer Perceptrons
Type of publication: Conference paper
Citation: Moerland-96.3
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.
Userfields: dates={August 29--31}, ipdmembership={learning},
Keywords: activation function, liquid crystal light valve (LCLV), non-negative neural networks, optical multilayer perceptron, weight discretization
Projects Idiap
Authors Moerland, Perry
Fiesler, Emile
Saxena, Indu
Added by: [UNK]
Total mark: 0