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Gain and exposure scheduling to compensate for photorefractive neural-network weight decay

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Abstract

A gain and exposure schedule that theoretically eliminates the effect of photorefractive weight decay for the general class of outer-product neural-network learning algorithms (e.g., backpropagation, Widrow – Hoff, perceptron) is presented. This schedule compensates for photorefractive diffraction-efficiency decay by iteratively increasing the spatial-light-modulator transfer function gain and decreasing the weight-update exposure time. Simulation results for the scheduling procedure, as applied to backpropagation learning for the exclusive-or problem, show improved learning performance compared with results for networks trained without scheduling.

© 1995 Optical Society of America

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