SPGD algorithm with adaptive gain

被引：0
|
作者：
Fang Z. [1 ]
Xu X. [2 ]
Li X. [2 ]
Liu J. [2 ]
Yang H. [2 ]
Gong C. [2 ]
机构：
[1] School of Information and Control Engineering, China University of Mining and Technology, Xuzhou
[2] School of Electronic Engineering, Jiangsu Ocean University, Lianyungang
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2020年 / 49卷 / 10期
关键词：
Adam; Adaptive optics; DM; SPGD; Wavefront correction;
D O I：
10.3788/IRLA20200274
中图分类号：
学科分类号：
摘要：
SPGD is a control algorithm widely used in wavefront sensorless adaptive optics (AO) systems. The gain is commonly set to a fixed value in the traditional SPGD algorithm. With the increase of the number of DM elements, which can easily lead to the slow convergence speed of the algorithm and the increase of the probability of falling into the local extreme value. Adam optimizer is an optimized stochastic gradient descent algorithm commonly used in deep learning. It has the advantage of achieving adaptive learning rate. The advantages of Adam optimizer adaptive gain and SPGD algorithm were combined to realize adaptive gain for AO system control. The simulation model of wavefront sensorless AO system was established with 32, 61, 97 and 127 elements DM as wavefront correction devices respectively, wavefront aberrations with different turbulence intensities as correction objects. The results show that the optimized algorithm converges faster than basic SPGD algorithm and the probability of falling into local extremum decreases. As the number of DM elements increases and the turbulence intensity increases, the advantages of the optimized algorithm are more obvious. The above research results provide a theoretical basis for the practical application of the SPGD algorithm based on Adam optimization. Copyright ©2020 Infrared and Laser Engineering. All rights reserved.
引用
收藏
相关论文
共 11 条
  • [1] Song J, Li Y, Che D, Et al., Coherent beam combining based on the SPGD algorithm with a momentum term, Optik, 202, (2020)
  • [2] Huang G, Geng C, Li F, Et al., Adaptive SMF coupling based on precise-delayed SPGD algorithm and its application in free space optical communication, IEEE Photonics Journal, 10, 3, pp. 1-12, (2018)
  • [3] Zhou H Q, Hang L L, Wang Y T., Deep learning algorithm and its application in optics, Infrared and Laser Engineering, 48, 12, (2019)
  • [4] Yang Huizhen, Wang Bin, Liu Ruiming, Et al., Analysis of anti-noise capability of model-based wavefront sensorless adaptive optics system, Infrared and Laser Engineering, 46, 8, (2017)
  • [5] Wang Weibing, Wang Tingfeng, Guo Jin, Simulation on the law of wave-front shaping with stochastic parallel gradient descent algorithm for adaptive optic, Chinese Journal of Optics, 7, 3, pp. 411-420, (2014)
  • [6] Yang Huizhen, Chen Bo, Li Xinyang, Et al., Experimental demonstration of Stochastic Parallel Gradient Descent Control Algorithm for adaptive optics system, Acta Optica Sinica, 2, 6, pp. 205-210, (2008)
  • [7] Kingma Diederik P, Ba Jimmy, Adam: A method for stochastic optimization, 3rd International Conference for Learning Representations, (2015)
  • [8] Huang L H, Cao L H, Li N, Et al., A state perception method for infrared dim and small targets with deep learning, Chinese Optics, 13, 3, pp. 527-536, (2020)
  • [9] Fan L L, Zhao H W, Zhao H Y., Survey of target detection based on deep convolutional neural networks, Optics and Precision Engineering, 28, 5, pp. 1152-1164, (2020)
  • [10] Yang G, Liu L, Jiang Z, Et al., Incoherent beam combining based on the momentum SPGD algorithm, Opt Laser Technol, 101, pp. 372-378, (2018)
12