Investigation into effects of device variation on performance of optoelectronic neural networks

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Abstract
Free space optical links, using LEDs and photodiodes, have been studied as a means of communication between neural layers in an analogue multilayered perceptron neural network architecture, trained using the back-propagation learning algorithm. Simulation results on network learning and generalisation show that such networks are tolerant to as much as 75% variation in either the LED or photodiode responses.

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