Journal
QUANTUM ELECTRONICS
Volume 51, Issue 12, Pages 1118-1121Publisher
TURPION LTD
DOI: 10.1070/QEL17655
Keywords
nonlinear Schrodinger equation; inverse scattering problem method; Zakharov-Shabat problem; nonlinear Fourier transform; neural networks; machine learning
Funding
- RF President's Grants Council (State Support to Young Russian Scientists Programme) [MK-677.2020.9, FSUS-2020-0034]
- Leverhulme Trust [RPG-2018-063]
Ask authors/readers for more resources
A neural network architecture is proposed for predicting a continuous nonlinear spectrum of optical signals and performing inverse NFT for signal modulation. The average relative error in predicting the spectrum during direct NFT calculation is 2.68 x 10(-3), while the average relative error in predicting the signal for inverse NFT is 1.62 x 10(-4).
A neural network architecture is proposed that allows a continuous nonlinear spectrum of optical signals to be predicted and an inverse nonlinear Fourier transform (NFT) to he 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 x 10(-3). and the average value of the relative error in predicting the signal for the inverse NFT is 1.62 x 10(-4).
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
