中国科学院长春光学精密机械与物理研究所

QQ客服 官方微博 反馈留言

	Numerical and experimental study on coherent beam combining using an improved stochastic parallel gradient descent algorithm
	J. K. Song,Y. Y. Li,D. B. Che and T. F. Wang
	2020
发表期刊	Laser Physics
ISSN	1054-660X
卷号	30期号:8页码:7
摘要	The adaptive gradient (AdaGrad) method is an optimization algorithm widely used in the field of artificial intelligence. An adaptive gradient stochastic parallel gradient descent (SPGD) algorithm (AdaSPGD algorithm), combining an AdaGrad algorithm with an SPGD algorithm, is innovatively introduced and implemented in coherent beam synthesis. The performance of a coherent beam combination system utilizing the AdaSPGD method is validated by numerical simulation of straightening static phase aberrations. The results of the simulations indicate that the AdaSPGD algorithm not only can effectively solve the trouble of difficulty in selecting the gain coefficient in the actual beam combining system, but also can accelerate the convergence of the phase control algorithm. Furthermore, the effectiveness of the proposed algorithm is demonstrated by means of the experimental investigation on coherent beam synthesis of a two-channel fiber array. The AdaSPGD algorithm is a satisfactory modification of the conventional SPGD algorithm.
DOI	10.1088/1555-6611/ab9118
URL	查看原文
收录类别	SCI ; EI
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/64735
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	J. K. Song,Y. Y. Li,D. B. Che and T. F. Wang. Numerical and experimental study on coherent beam combining using an improved stochastic parallel gradient descent algorithm[J]. Laser Physics,2020,30(8):7.
APA	J. K. Song,Y. Y. Li,D. B. Che and T. F. Wang.(2020).Numerical and experimental study on coherent beam combining using an improved stochastic parallel gradient descent algorithm.Laser Physics,30(8),7.
MLA	J. K. Song,Y. Y. Li,D. B. Che and T. F. Wang."Numerical and experimental study on coherent beam combining using an improved stochastic parallel gradient descent algorithm".Laser Physics 30.8(2020):7.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Numerical and experi（1225KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览 下载

个性服务

推荐该条目

保存到收藏夹

查看访问统计

导出为Endnote文件

谷歌学术

谷歌学术中相似的文章

[J. K. Song,Y. Y. Li,D. B. Che and T. F. Wang]的文章

百度学术

百度学术中相似的文章

[J. K. Song,Y. Y. Li,D. B. Che and T. F. Wang]的文章

必应学术

必应学术中相似的文章

[J. K. Song,Y. Y. Li,D. B. Che and T. F. Wang]的文章

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。