A Study of Optical PerceptronLearning Networks

• Main Authors: Chau-Jern Cheng, 鄭超仁
• Other Authors: Ken Yuh Hsu
• Format: Others
• Language: zh-TW
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/08222209424377172075

博士 === 國立交通大學 === 光電(科學)研究所 === 82 === The thesis investigates the principles, architectures, implementations and learning characteristics of optical perceptron netetworks. The perceptron model is realized by using the technique of photorefractctive dynamic hologram and optical perceptron is used for optical pattern recognition and classification. The interconnection of the network is construructed by the amplitude of photorefractive hologram. The dynamic characteristics of the photorefractive crystal is used for simulating the learning behaviors of the optical perceptron. In addition, we used an interferometric technique which combines a pair photodetectors with an electronic subtractor for implementing the bipolar optical perceptron. The convergence properties of the optical perceptron are derived and a guideline for the exposure schedule of the photorefractive perceptron learning is presented. A novel technique for trainable pattern classification using a computer generated hologram is proposed and demonstrated. The perceptron learning algorithm is used for training, and the trained interconnection weight is transferred into a computer-generated hologram. The hologram is used as a correlation filter in the optical system for pattern classification. Furthermore, the computer generated holographic technique is used in the optical recognition system for real-time image processing. On the other hand,we consider the properties of a generalized perceptron learning network taking into account the decay or gain of the weight during the training stages. A mathematical proof is given which shows the conditional convergence of the learning algorithm. We also described a modified learning algorithm which provides a solution to the problem of weight decay in optical perceptron due to hologram erasure.