Add to ORKGAll-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
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
Abstract Recently back propagation neural network BPNN has been applied successfully in many areas w...
I'' Abstract 'U The multi-layer perceptron is a type of feed forward neural network f...
All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examp...
The most promising approaches for optical neural networks are based on intensity encoding. However, ...
In order to implement fully adaptive optical multilayer neural networks, a number of issues involvin...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
Sigmoidlike activation functions, as available in analog hardware, differ in various ways from the s...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This p...
Click on the DOI link below to access the article (may not be free)The optical bench training of an ...
Neural networks as a general mechanism for learning and adaptation became increasingly popular in re...
This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very diffic...
The backpropagation algorithm is widely used for training multilayer neural networks. In this public...
Several neural network architectures have been developed over the past several years. One of the mos...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
Abstract Recently back propagation neural network BPNN has been applied successfully in many areas w...
I'' Abstract 'U The multi-layer perceptron is a type of feed forward neural network f...
All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examp...
The most promising approaches for optical neural networks are based on intensity encoding. However, ...
In order to implement fully adaptive optical multilayer neural networks, a number of issues involvin...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
Sigmoidlike activation functions, as available in analog hardware, differ in various ways from the s...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This p...
Click on the DOI link below to access the article (may not be free)The optical bench training of an ...
Neural networks as a general mechanism for learning and adaptation became increasingly popular in re...
This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very diffic...
The backpropagation algorithm is widely used for training multilayer neural networks. In this public...
Several neural network architectures have been developed over the past several years. One of the mos...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
Abstract Recently back propagation neural network BPNN has been applied successfully in many areas w...
I'' Abstract 'U The multi-layer perceptron is a type of feed forward neural network f...