Artificial Neural Networks for Data Analysis of Magnetic Measurements on East

doi:10.1007/s10894-015-0044-z

CORC > 合肥物质科学研究院 > 中国科学院合肥物质科学研究院 > 中科院等离子体物理研究所

	Artificial Neural Networks for Data Analysis of Magnetic Measurements on East
	Wang, Bo 1; Xiao, Bingjia 1,2; Li, Jiangang 1,2; Guo, Yong 2; Luo, Zhengping 2
刊名	JOURNAL OF FUSION ENERGY
	2016-04-01
卷号	35 期号:2 页码:390-400
关键词	Neural Networks Plasma Equilibrium Data Analysis
DOI	10.1007/s10894-015-0044-z
文献子类	Article
英文摘要	The problem of the reconstruction of the parameters characterizing the plasma shape in a tokamak device is of paramount importance both for present day experiments and for future reactor. The plasma shape can only be evaluated by diagnostic data, such as poloidal flux and magnetic field measured respectively by the flux loops and magnetic probes located on the vacuum vessel outside the plasma. The aim of the present paper is to take a step forward in the application of the neural network approach for the identification of non-circular plasma equilibrium and data analysis for the problem of the optimal location of a limited number of magnetic sensors. We have adopted a machine learning method, back-propagation neural network, to analyze the magnetic diagnostic data. The database has been generated by means of a specially adapted version of an MHD equilibrium code EFIT with reference to the EAST geometry and stored in the EAST mdsplus database. The network uses external magnetic measurements as input data and the selected plasma parameters as output data to train and test. Then a novel strategy is implemented for the selection of the optimum location of a limited number of magnetic probes based data analysis of the network. The average accuracy of the identification procedure is quite good (e.g., the maximum relative error is 0.260 % of internal inductance), with a contrast of the computation results of EFIT as desired output. It has been shown that the degradation of the performance is rather small (e.g., RMS error of minor radius vary from 4.307 to 4.765 %) when the number of magnetic probes is reduced by nearly half.
WOS关键词	STOCHASTIC-CONTROL ; TOKAMAK
WOS研究方向	Nuclear Science & Technology
语种	英语
WOS记录号	WOS:000371623500039
资助机构	National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Magnetic Confinement Fusion Research Program of China(2014GB103000) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216) ; National Natural Science Foundation of China(11305216)
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/21680]
专题	合肥物质科学研究院_中科院等离子体物理研究所
作者单位	1.Univ Sci & Technol China, Hefei 230027, Anhui, Peoples R China 2.Chinese Acad Sci, Inst Plasma Phys, Hefei 230031, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Wang, Bo,Xiao, Bingjia,Li, Jiangang,et al. Artificial Neural Networks for Data Analysis of Magnetic Measurements on East[J]. JOURNAL OF FUSION ENERGY,2016,35(2):390-400.
APA	Wang, Bo,Xiao, Bingjia,Li, Jiangang,Guo, Yong,&Luo, Zhengping.(2016).Artificial Neural Networks for Data Analysis of Magnetic Measurements on East.JOURNAL OF FUSION ENERGY,35(2),390-400.
MLA	Wang, Bo,et al."Artificial Neural Networks for Data Analysis of Magnetic Measurements on East".JOURNAL OF FUSION ENERGY 35.2(2016):390-400.