Share Email Print

Proceedings Paper

Self-organizing optical neural network for unsupervised learning
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

One of the features in neural computing must be the adaptability to changeable environment and to recognize unknown objects. This paper deals with an adaptive optical neural network using Kohonon's self-organizing feature map algorithm for unsupervised learning. A compact optical neural network of 64 neurons using liquid crystal televisions is used for this study. To test the performances of the self-organizing neural network, experimental demonstrations with computer simulations are provided. Effects due to unsupervised learning parameters are analyzed. We have shown that the optical neural network is capable of performing both unsupervised learning and pattern recognition operations simultaneously, by setting two matching scores in the learning algorithm. By using slower learning rate, the construction of the memory matrix becomes topologically more organized. Moreover, by introducing the forbidden regions in the memory space, it would enable the neural network to learn new patterns without erasing the old ones.

Paper Details

Date Published: 1 September 1990
PDF: 14 pages
Proc. SPIE 1296, Advances in Optical Information Processing IV, (1 September 1990); doi: 10.1117/12.21282
Show Author Affiliations
Thomas Taiwei Lu, The Pennsylvania State Univ. (United States)
Francis T. S. Yu, The Pennsylvania State Univ. (United States)
Don A. Gregory, U.S. Army Missile Command (United States)

Published in SPIE Proceedings Vol. 1296:
Advances in Optical Information Processing IV
Dennis R. Pape, Editor(s)

© SPIE. Terms of Use
Back to Top