Analog Optical Neural Nets: A Noise Sensitivity Analysis
Final technical rept. 21 Jul 1989-20 Jul 1991
BDM INTERNATIONAL INC MCLEAN VA
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The development of analog optical implementations of neural networks such as the multilayer perceptron with learning by backward error propagation BEP requires an understanding of the noise sensitivity of such architectures. In this program computer simulations were used to study the effects of component and system noise on the performance of such optical implementations. A hybrid opticalelectronic parallel architecture capable of both the forward pass and backward error propagation steps of training data presentation was conceived and modeled. The simulations showed that the most significant effects were due to the nonlinear response of the spatial light modulators used to store and update the neural weights. Another conclusion of the simulation results was that increasing the hidden layer size increases noise immunity significantly.