• Laser & Optoelectronics Progress
  • Vol. 56, Issue 12, 122201 (2019)
Dongting Hu1、2, Wen Shen1、2, Wenchao Ma1、2, Xinyu Liu1、2, Zhouping Su1、2, Huaxin Zhu1、2, Xiumei Zhang1、2, Lizhi Que1、2, Zhuowei Zhu1、2, Yixin Zhang1、2, Guoqing Chen1、2, and Lifa Hu1、2、*
Author Affiliations
  • 1 School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China
  • 2 Jiangsu Provincial Research Center of Light Industrial Opto-Electronic Engineering and Technology, Wuxi, Jiangsu 214122, China
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    DOI: 10.3788/LOP56.122201 Cite this Article Set citation alerts
    Dongting Hu, Wen Shen, Wenchao Ma, Xinyu Liu, Zhouping Su, Huaxin Zhu, Xiumei Zhang, Lizhi Que, Zhuowei Zhu, Yixin Zhang, Guoqing Chen, Lifa Hu. Fast Convergence Stochastic Parallel Gradient Descent Algorithm[J]. Laser & Optoelectronics Progress, 2019, 56(12): 122201 Copy Citation Text show less

    Abstract

    In this paper, based on deformable mirrors and the stochastic parallel-gradient-descent (SPGD) algorithm, an adaptive optics system (AOS) without wavefront detection is theoretically simulated. In order to improve the convergence speed of the AOS without reducing its accuracy, this paper optimizes the relationship between the amplitude of random perturbation and the gain coefficient in the SPGD algorithm. The experiment conducted in this study shows that the AOS has a parameter preference area, which is related to the initial distortion magnitude. Furthermore, the results of the theoretical verification and the comparison with that by the simulated annealing algorithm reveal that the convergence accuracy of the SPGD algorithm is 6.32% higher than that of the SA algorithm and the SPGD algorithm has a larger convergence speed.
    Dongting Hu, Wen Shen, Wenchao Ma, Xinyu Liu, Zhouping Su, Huaxin Zhu, Xiumei Zhang, Lizhi Que, Zhuowei Zhu, Yixin Zhang, Guoqing Chen, Lifa Hu. Fast Convergence Stochastic Parallel Gradient Descent Algorithm[J]. Laser & Optoelectronics Progress, 2019, 56(12): 122201
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