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Stochastic parallel gradient descent optimization based on decoupling of the software and hardware
Fu, Qiang1,2,3,4; Pott, Jörg-Uwe1; Shen, Feng2,3; Rao, Changhui2,3; Fu, Q. (fu@mpia.de)
Source PublicationOptics Communications
Volume310Pages:138-149
2014
Language英语
ISSN00304018
DOI10.1016/j.optcom.2013.07.045
Indexed BySCI ; Ei
WOS IDWOS:000328304600024
Subtype期刊论文
AbstractWe 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.; 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.
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Document Type期刊论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/6381
Collection自适应光学技术研究室(八室)
Corresponding AuthorFu, Q. (fu@mpia.de)
Affiliation1. 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
GB/T 7714
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.
APA Fu, Qiang,Pott, Jörg-Uwe,Shen, Feng,Rao, Changhui,&Fu, Q. .(2014).Stochastic parallel gradient descent optimization based on decoupling of the software and hardware.Optics Communications,310,138-149.
MLA Fu, Qiang,et al."Stochastic parallel gradient descent optimization based on decoupling of the software and hardware".Optics Communications 310(2014):138-149.
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