Adaptive Optical Radial Basis Function Neural Network Classifier.

Abstract : An adaptive optical radial basis function neural network classifier is experimentally demonstrated. We describe a spatially multiplexed system incorporating on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input vector and 198 stored reference patterns in parallel using dual vector-matrix multipliers and a contrast-reversing spatial light modulator. Software is used to emulate an analog electronic chip that performs the on-line learning of the weights and basis function widths. An experimental recognition rate of 92.7% correct out of 300 testing samples is achieved with the adaptive training versus 31.0% correct for non-adaptive training. We compare the experimental results with a detailed computer model of the system in order to analyze the influence of various noise sources on the system performance. (KAR) P. 3

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