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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.
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