光学学报, 2007, 27 (8): 1355, 网络出版: 2007-09-05
自适应光学系统随机并行梯度下降控制算法仿真与分析
Simulation and Analysis of Stochastic Parallel Gradient Descent Control Algorithm for Adaptive Optics System
自适应光学 随机并行梯度下降算法 数值仿真 变形镜 收敛 adaptive optics stochastic parallel gradient descent algorithm numerical simulation deformable mirror convergence
摘要
随机并行梯度下降算法能不依赖波前传感器直接对系统性能进行优化。以32单元变形镜为校正器,采用随机并行梯度下降算法建立了自适应光学系统仿真模型。通过分析该系统对静态波前畸变的校正能力,验证了随机并行梯度下降算法的收敛性;讨论了算法增益系数、随机扰动幅度与收敛速度的关系,并指出通过算法增益系数的自适应调整可以改进算法的收敛速度。
Abstract
The stochastic parallel gradient descent (SPGD) algorithm can optimize the system performance directly, while being independent of wave-front sensor. Based on SPGD algorithm, an adaptive optics system model with a 32-element deformable mirror was simulated. Convergence of SPGD algorithm was verified through analyzing correction capabilities for static wave-front aberrations. The relationship of algorithm gain coefficient, stochastic perturbation amplitude and convergence rate were discussed. Convergence rate can be improved by adaptive adjustment of algorithm gain coefficient.
杨慧珍, 李新阳, 姜文汉. 自适应光学系统随机并行梯度下降控制算法仿真与分析[J]. 光学学报, 2007, 27(8): 1355. 杨慧珍, 李新阳, 姜文汉. Simulation and Analysis of Stochastic Parallel Gradient Descent Control Algorithm for Adaptive Optics System[J]. Acta Optica Sinica, 2007, 27(8): 1355.
PDF全文
