Shift-invariant interpattern association neural network

Chii Maw Uang; Shizhuo Yin; P. Andres; Wade Reeser; Francis T.S. Yu

doi:10.1364/AO.33.002147

Shift-invariant interpattern association neural network

Chii Maw Uang, Shizhuo Yin, P. Andres, Wade Reeser, Francis T.S. Yu

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

A shift-invariant neural network that uses the translation-invariant property of the modulus Fourier spectra with the heteroassociation interpattern association memory is proposed. A binary encoding of a spectral sampling of the training set is used to preserve the main features. Computer simulations and experimental demonstrations are provided that show the shift-invariant property of the proposed optical neural network.

Original language	English (US)
Pages (from-to)	2147-2151
Number of pages	5
Journal	Applied optics
Volume	33
Issue number	11
DOIs	https://doi.org/10.1364/AO.33.002147
State	Published - Apr 10 1994

All Science Journal Classification (ASJC) codes

Atomic and Molecular Physics, and Optics
Engineering (miscellaneous)
Electrical and Electronic Engineering

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10.1364/AO.33.002147

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title = "Shift-invariant interpattern association neural network",

abstract = "A shift-invariant neural network that uses the translation-invariant property of the modulus Fourier spectra with the heteroassociation interpattern association memory is proposed. A binary encoding of a spectral sampling of the training set is used to preserve the main features. Computer simulations and experimental demonstrations are provided that show the shift-invariant property of the proposed optical neural network.",

author = "Uang, {Chii Maw} and Shizhuo Yin and P. Andres and Wade Reeser and Yu, {Francis T.S.}",

year = "1994",

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language = "English (US)",

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AU - Yin, Shizhuo

AU - Andres, P.

AU - Reeser, Wade

AU - Yu, Francis T.S.

PY - 1994/4/10

Y1 - 1994/4/10

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AB - A shift-invariant neural network that uses the translation-invariant property of the modulus Fourier spectra with the heteroassociation interpattern association memory is proposed. A binary encoding of a spectral sampling of the training set is used to preserve the main features. Computer simulations and experimental demonstrations are provided that show the shift-invariant property of the proposed optical neural network.

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JO - Applied optics

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Shift-invariant interpattern association neural network

Abstract

All Science Journal Classification (ASJC) codes

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