Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup
| Autor: | Bing Xu, Ping Yang, Wang Zhao, Tao Cheng, Chunxuan Su, Kangjian Yang, Ruiyan Jin, Shiqing Ma, Boheng Lai |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
beam cleanup
Computer science Stability (learning theory) Phase (waves) slab laser Laser stochastic parallel gradient descent algorithm Atomic and Molecular Physics and Optics TA1501-1820 Power (physics) law.invention Control theory law Convergence (routing) Applied optics. Photonics Radiology Nuclear Medicine and imaging Laser beam quality Gradient descent Instrumentation Beam (structure) |
| Zdroj: | Photonics Volume 8 Issue 5 Photonics, Vol 8, Iss 165, p 165 (2021) |
| ISSN: | 2304-6732 |
| DOI: | 10.3390/photonics8050165 |
| Popis: | For a high-power slab solid-state laser, obtaining high output power and high output beam quality are the most important indicators. Adaptive optics systems can significantly improve beam qualities by compensating for the phase distortions of the laser beams. In this paper, we developed an improved algorithm called Adaptive Gradient Estimation Stochastic Parallel Gradient Descent (AGESPGD) algorithm for beam cleanup of a solid-state laser. A second-order gradient of the search point was introduced to modify the gradient estimation, and it was introduced with the adaptive gain coefficient method into the classical Stochastic Parallel Gradient Descent (SPGD) algorithm. The improved algorithm accelerates the search for convergence and prevents it from falling into a local extremum. Simulation and experimental results show that this method reduces the number of iterations by 40%, and the algorithm stability is also improved compared with the original SPGD method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|