Improvement of the generalization capability for a pattern-recognition neural network that uses a Gaussian-synapse neuron model

Abstract

A method for improving the generalization capability for optical pattern recognition by use of a Gaussian-synapse neuron model is discussed. By the dispersive effect of the Gaussian function the input images are blurred and then fed into a multilayer neural network for learning and recognition. The effectiveness of this method is demonstrated in two-dimensional shift- and scale-invariant optical pattern recognition.

© 2000 Optical Society of America

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