Acta Optica Sinica, Volume. 31, Issue s1, 100408(2011)
Adaptive Optimization of Stochastic Parallel Gradient Descent Algorithm
The stochastic parallel gradient descent (SPGD) algorithm had been proved to be an effective system control method of image clarity in experiments and applications, which is independent of wavefront sensor and can optimize the system performance directly. The convergence speed and stability are determined by the value of the control parameters. The ranges of the control parameters are narrow, and out of range will lead to the vibration of the convergence or reduce the convergence speed. Based on SPGD, an image clarity test bed is built with a 52-element deformable mirror and a position sensitive detector. A method of automatic adjustment of parameters is proposed. The principle of SPGD control algorithm was demonstrated through examining the effects of gain and perturbation amplitude on correction capability and convergence rate, and the new method is proved to be effective. Experimental results show that by using the method of automatic adjustment of parameters, the ranges of parameters are extended. The practicality and convergence speed of the algorithm are improved with better convergence stability.
Get Citation
Copy Citation Text
Liang Yu, Huang Yongmei, Qi Bo, Bian Jiang, Wu Qiongyan. Adaptive Optimization of Stochastic Parallel Gradient Descent Algorithm[J]. Acta Optica Sinica, 2011, 31(s1): 100408
Category: Fourier optics and signal processing
Received: Dec. 22, 2010
Accepted: --
Published Online: Jun. 23, 2011
The Author Email: Yu Liang (liangyu66@gmail.com)