Publication

Discrete All-Positive Multilayer Perceptrons for Optical Implementation

1998
Journal Articles

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.

Official source

https://infoscience.epfl.ch/entities/publication/5c04f667-d5a4-4a69-8bb8-8e265f40efc5

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1998
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/5c04f667-d5a4-4a69-8bb8-8e265f40efc5

About this result

Related concepts (32)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

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Discrete All-Positive Multilayer Perceptrons for Optical Implementation

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