POPART: partial optical implementation of adaptive resonance theory 2

Adaptive resonance architectures are neural nets that are capable of classifying arbitrary input patterns into stable category representations. A hybrid optoelectronic implementation utilizing an optical joint transform correlator is proposed and demonstrated. The resultant optoelectronic system is...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 4; no. 4; pp. 695 - 702
Main Authors Kane, J.S., Paquin, M.J.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.07.1993
Institute of Electrical and Electronics Engineers
Subjects
Adaptive optics
Applied sciences
Computer architecture
Correlators
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Neural networks
Optical computing
Optical filters
Optical signal processing
Resonance
Signal processing algorithms
Subspace constraints
Optical correlator
Processing time
Optoelectronics
Resonance
Neural network
Image processing
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Summary:Adaptive resonance architectures are neural nets that are capable of classifying arbitrary input patterns into stable category representations. A hybrid optoelectronic implementation utilizing an optical joint transform correlator is proposed and demonstrated. The resultant optoelectronic system is able to reduce the number of calculations compared to a strictly computer-based approach. The result is that, for larger images, the optoelectronic system is faster than the computer-based approach.< >
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ISSN:1045-9227
1941-0093
DOI:10.1109/72.238323