On-Demand Phase Control of a 7-Fiber Amplifiers Array with Neural Network and Quasi-Reinforcement Learning
Abstract
:1. Introduction
2. Neural Network in a Phase Reduction Loop with Quasi-Reinforcement Learning
3. Target Adaptive NN with QRL Process
Algorithm 1: Quasi-reinforcement learning algorithm for TANN |
Input: Measurement model: , reward function Output:Trained target adaptive neural network TANN:
|
4. Simulations
5. Experiments
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Shpakovych, M.; Maulion, G.; Boju, A.; Armand, P.; Barthélémy, A.; Desfarges-Berthelemot, A.; Kermene, V. On-Demand Phase Control of a 7-Fiber Amplifiers Array with Neural Network and Quasi-Reinforcement Learning. Photonics 2022, 9, 243. https://doi.org/10.3390/photonics9040243
Shpakovych M, Maulion G, Boju A, Armand P, Barthélémy A, Desfarges-Berthelemot A, Kermene V. On-Demand Phase Control of a 7-Fiber Amplifiers Array with Neural Network and Quasi-Reinforcement Learning. Photonics. 2022; 9(4):243. https://doi.org/10.3390/photonics9040243
Chicago/Turabian StyleShpakovych, Maksym, Geoffrey Maulion, Alexandre Boju, Paul Armand, Alain Barthélémy, Agnès Desfarges-Berthelemot, and Vincent Kermene. 2022. "On-Demand Phase Control of a 7-Fiber Amplifiers Array with Neural Network and Quasi-Reinforcement Learning" Photonics 9, no. 4: 243. https://doi.org/10.3390/photonics9040243