Self-organizing optical neural network for unsupervised learning

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.