Abstract

High-order neural networks have been shown to have impressive computational, storage, and learning capabilities. This performance is because the order or structure of a high-order neural network can be tailored to the order or structure of a problem. Thus, a neural network designed for a particular class of problems becomes specialized but also very efficient in solving those problems. Furthermore, a priori knowledge, such as geometric invariances, can be encoded in high-order networks. Because this knowledge does not have to be learned, these networks are very efficient in solving problems that utilize this knowledge.

© 1987 Optical Society of America

Optical implementation of a translation-invariant second-order neural network for multiple-pattern classification

Sunao Kakizaki, Paul Horan, Akira Arimoto, Hiroshi Sako, Atsushi Saito, and Fumio Kugiya
Appl. Opt. 33(35) 8270-8280 (1994)

Optical implementation of a second-order translation-invariant network algorithm

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Shift-invariant interpattern association neural network

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Multilayer optical learning networks

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