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Optics Letters

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  • Vol. 28, Iss. 24 — Dec. 15, 2003
  • pp: 2524–2526

Coherent optical neural network that learns desirable phase values in the frequency domain by use of multiple optical-path differences

Sotaro Kawata and Akira Hirose  »View Author Affiliations


Optics Letters, Vol. 28, Issue 24, pp. 2524-2526 (2003)
http://dx.doi.org/10.1364/OL.28.002524


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Abstract

A coherent optical neural network is proposed that has the learning ability to achieve desirable phase values in the frequency domain. It is composed of multiple optical-path differences whose lengths are different from one another. The system learns a phase value at each discrete position in the frequency domain by obeying the complex-valued Hebbian rule. The learning curve also agrees with theoretical evolution.

© 2003 Optical Society of America

OCIS Codes
(010.1080) Atmospheric and oceanic optics : Active or adaptive optics
(070.1170) Fourier optics and signal processing : Analog optical signal processing
(120.5060) Instrumentation, measurement, and metrology : Phase modulation
(200.4260) Optics in computing : Neural networks
(200.4700) Optics in computing : Optical neural systems
(350.2460) Other areas of optics : Filters, interference

Citation
Sotaro Kawata and Akira Hirose, "Coherent optical neural network that learns desirable phase values in the frequency domain by use of multiple optical-path differences," Opt. Lett. 28, 2524-2526 (2003)
http://www.opticsinfobase.org/ol/abstract.cfm?URI=ol-28-24-2524


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