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题名:
Stochastic parallel gradient descent optimization based on decoupling of the software and hardware
作者: Fu, Qiang1,2,3,4; Pott, Jörg-Uwe1; Shen, Feng2,3; Rao, Changhui2,3
刊名: Optics Communications
出版日期: 2014
卷号: 310, 页码:138-149
学科分类: Atmospheric turbulence - Gradient methods - Sensors - Signal distortion
DOI: 10.1016/j.optcom.2013.07.045
通讯作者: Fu, Q. (fu@mpia.de)
文章类型: 期刊论文
中文摘要: 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. © 2013 Elsevier B.V. All rights reserved.
英文摘要: 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. © 2013 Elsevier B.V. All rights reserved.
收录类别: SCI ; Ei
项目资助者: MPG-CAS graduate studentship
语种: 英语
WOS记录号: WOS:000328304600024
ISSN号: 00304018
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/6381
Appears in Collections:自适应光学技术研究室(八室)_期刊论文

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作者单位: 1. Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg, Germany
2. Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Science, Chengdu 610209, China
3. Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China
4. Graduate School of Chinese Academy of Science, Beijing 100039, China

Recommended Citation:
Fu, Qiang,Pott, Jörg-Uwe,Shen, Feng,et al. Stochastic parallel gradient descent optimization based on decoupling of the software and hardware[J]. Optics Communications,2014,310:138-149.
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