Discrete All-Positive Multilayer Perceptrons for Optical Implementation
| Type of publication: | Idiap-RR |
| Citation: | Moerland-97.2 |
| Number: | Idiap-RR-02-1997 |
| Year: | 1997 |
| Institution: | IDIAP |
| Note: | Accepted for publication in {\em Optical Engineering} |
| 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. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
