Stochastic parallel gradient descent algorithm for adaptive optics system

被引：1

作者：

Ma H. ^{[1
]}

Zhang P. ^{[1
]}

Zhang J. ^{[1
]}

Fan C. ^{[1
]}

Wang Y. ^{[1
]}

机构：

[1] Key Laboratory of Atmospheric Composition and Optical Radiation, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences

来源：

Qiangjiguang Yu Lizishu/High Power Laser and Particle Beams | 2010年 / 22卷 / 06期

关键词：

Aberration; Adaptive optics; Numerical simulation; Stochastic parallel gradient descent algorithm;

D O I：

10.3788/HPLPB20102206.1206

中图分类号：

学科分类号：

摘要：

The stochastic parallel gradient descent(SPGD) algorithm can optimize the system performance indexes directly to correct wavefront aberration. A 61-element adaptive optics system model based on SPGD algorithm was simulated. For different initial static aberrations, the algorithm's correction capabilities were analyzed. The selections of algorithm gain coefficient and perturbation amplitude were compared in the conditions of adopting different performance indexes, and so was the correction effects. Simulation results demonstrate that the algorithm's convergence rate depends on gain coefficient and perturbation amplitude to a great extent. For relatively severe aberrations with perturbation amplitude ranging from 0.50 to 0.85, the correction effects of using mean radius as the system performance index is better than that of using Strehl ratio.

引用

页码：1206 / 1210

页数：4

共 8 条

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