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Neural network model using interpattern association

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Abstract

This paper investigates a neural network model—interpattern association (IPA) model—in which the basic logical operations are used to determine the interpattern association (i.e., association between the reference patterns), and simple logical rules are applied to construct tristate interconnections in the network. Computer simulations for the reconstruction of similar English letters embedded in the random noise by the IPA model have shown improved performance compared with the Hopfield model. A 2-D hybrid optical neural network is used to demonstrate the usefulness of the IPA model. Since there are only three gray levels used in the interconnection weight matrix for the IPA model, the dynamic range imposed on a spatial light modulator is rather relaxed, and the interconnections are much simpler than the Hopfield model.

© 1990 Optical Society of America

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