Coherent optical neural network that learns desirable phase values in the frequency domain by use of multiple optical-path differences.
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.
Kawata
Sotaro
S
Department of Frontier Informatics, Graduate School of Frontier Sciences, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. kawata@eis.t.u-tokyo.ac.jp
Hirose
Akira
A