期刊论文详细信息
OPTICS COMMUNICATIONS 卷:310
Stochastic parallel gradient descent optimization based on decoupling of the software and hardware
Article
Fu, Qiang1,2,3,4  Pott, Joerg-Uwe1  Shen, Feng2,3  Rao, Changhui2,3 
[1] Max Planck Inst Astron, D-69117 Heidelberg, Germany
[2] Chinese Acad Sci, Inst Opt & Elect, Lab Adapt Opt, Chengdu 610209, Peoples R China
[3] Chinese Acad Sci, Key Lab Adapt Opt, Chengdu 610209, Peoples R China
[4] Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China
关键词: Model free;    Model-free control;    SPGD;    Shack-Hartmann sensor;    Decoupling methods;   
DOI  :  10.1016/j.optcom.2013.07.045
来源: Elsevier
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【 摘 要 】

We classified the decoupled stochastic parallel gradient descent (SPGD) optimization model into two different types: software and hardware decoupling methods. A kind of software decoupling method is then proposed and a kind of hardware decoupling method is also proposed depending on the Shack-Hartmann (S-H) sensor. Using the normal sensor to accelerate the convergence of algorithm, the hardware decoupling method seems a capable realization of decoupled method. Based on the numerical simulation for correction of phase distortion in atmospheric turbulence, our methods are analyzed and compared with basic SPGD model and also other decoupling models, on the aspects of different spatial resolutions, mismatched control channels and noise. The results show that the phase distortion can be compensated after tens iterations with a strong capacity of noise tolerance in our model. (C) 2013 Elsevier B.V. All rights reserved.

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