You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
1 September 1990Self-organizing optical neural network for unsupervised learning
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.
The alert did not successfully save. Please try again later.
Thomas Taiwei Lu, Francis T. S. Yu, Don A. Gregory, "Self-organizing optical neural network for unsupervised learning," Proc. SPIE 1296, Advances in Optical Information Processing IV, (1 September 1990); https://doi.org/10.1117/12.21282