Neural network for calculating direct and inverse nonlinear Fourier transform

E. V. Sedov; Игорь Сергеевич Чеховской; Jaroslaw E. Prilepsky

doi:10.1070/QEL17655

Neural network for calculating direct and inverse nonlinear Fourier transform

E. V. Sedov, Игорь Сергеевич Чеховской, Jaroslaw E. Prilepsky

Результат исследования: Научные публикации в периодических изданиях › статья › рецензирование

Аннотация

Aneural network architecture is proposed that allows a continuous nonlinear spectrum of optical signals to be predicted and an inverse nonlinear Fourier transform (NFT) to be performed for signal modulation. The average value of the relative error in predicting the continuous spectrum by the neural network when calculating the direct NFT is found to be 2.68 10−3, and the average value of the relative error in predicting the signal for the inverse NFT is 1.62 10−4.

Язык оригинала	английский
Страницы (с-по)	1118-1121
Число страниц	4
Журнал	Quantum Electronics
Том	51
Номер выпуска	12
DOI	https://doi.org/10.1070/QEL17655
Состояние	Опубликовано - дек. 2021

Предметные области OECD FOS+WOS

2.02 ЭЛЕКТРОТЕХНИКА, ЭЛЕКТРОННАЯ ТЕХНИКА, ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ
2.05 ТЕХНОЛОГИЯ МАТЕРИАЛОВ
1.03 ФИЗИЧЕСКИЕ НАУКИ И АСТРОНОМИЯ

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10.1070/QEL17655

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title = "Neural network for calculating direct and inverse nonlinear Fourier transform",

abstract = "Aneural network architecture is proposed that allows a continuous nonlinear spectrum of optical signals to be predicted and an inverse nonlinear Fourier transform (NFT) to be performed for signal modulation. The average value of the relative error in predicting the continuous spectrum by the neural network when calculating the direct NFT is found to be 2.68 10−3, and the average value of the relative error in predicting the signal for the inverse NFT is 1.62 10−4.",

author = "Sedov, {E. V.} and Чеховской, {Игорь Сергеевич} and Prilepsky, {Jaroslaw E.}",

note = "The study was supported by the RF President's Grants Council (State Support to Young Russian Scientists Programme, Grant No. MK-677.2020.9) . The work of I.S. Chekhovskoy was supported by the state assignment for fundamental research FSUS-2020-0034. The work of J.E. Prilepsky was supported by the Leverhulme Trust (Project RPG-2018-063) . Publisher Copyright: {\textcopyright} 2021 Kvantovaya Elektronika and IOP Publishing Limited",

year = "2021",

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doi = "10.1070/QEL17655",

language = "English",

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Neural network for calculating direct and inverse nonlinear Fourier transform. / Sedov, E. V.; Чеховской, Игорь Сергеевич; Prilepsky, Jaroslaw E.

В: Quantum Electronics, Том 51, № 12, 12.2021, стр. 1118-1121.

Результат исследования: Научные публикации в периодических изданиях › статья › рецензирование

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AU - Sedov, E. V.

AU - Чеховской, Игорь Сергеевич

AU - Prilepsky, Jaroslaw E.

N1 - The study was supported by the RF President's Grants Council (State Support to Young Russian Scientists Programme, Grant No. MK-677.2020.9) . The work of I.S. Chekhovskoy was supported by the state assignment for fundamental research FSUS-2020-0034. The work of J.E. Prilepsky was supported by the Leverhulme Trust (Project RPG-2018-063) . Publisher Copyright: © 2021 Kvantovaya Elektronika and IOP Publishing Limited

PY - 2021/12

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N2 - Aneural network architecture is proposed that allows a continuous nonlinear spectrum of optical signals to be predicted and an inverse nonlinear Fourier transform (NFT) to be performed for signal modulation. The average value of the relative error in predicting the continuous spectrum by the neural network when calculating the direct NFT is found to be 2.68 10−3, and the average value of the relative error in predicting the signal for the inverse NFT is 1.62 10−4.

AB - Aneural network architecture is proposed that allows a continuous nonlinear spectrum of optical signals to be predicted and an inverse nonlinear Fourier transform (NFT) to be performed for signal modulation. The average value of the relative error in predicting the continuous spectrum by the neural network when calculating the direct NFT is found to be 2.68 10−3, and the average value of the relative error in predicting the signal for the inverse NFT is 1.62 10−4.

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Neural network for calculating direct and inverse nonlinear Fourier transform

Аннотация

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