人工神经网络在HL-2A装置汤姆逊散射数据处理中的应用
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摘要
人工神经网络是一种强大的非线性数据分析算法,其中的感知器神经网络第一次被用于处理HL-2A装置上汤姆逊散射系统的电子温度数据。采用输入层、隐藏层和输出层等三层神经网络结构,输入层为标定数据或测量数据,隐藏层使用sigmoid函数作为传递函数,输出层为电子温度值。从数据处理结果可以看出,该计算方法与传统的χ~2最小值方法计算的结果吻合,能够得到可靠的电子温度数据。而且由于计算温度时采用矩阵计算,计算速度比使用χ~2最小值法提高20倍以上,为将来利用汤姆逊散射测量的电子温度数据实现等离子体剖面实时反馈控制提供了可能。
Artificial neural network(NN)as a powerful nonlinear data processing method,has been successfully applied to process electron temperature for Thomson scattering system on HL-2 A.A type of perception is chosen.The NN has three layers:input layer,hidden layer,and output layer.Calibration data or measured data are the input layer,hidden layer uses sigmoid function as transfer function,and output layer is electron temperature.The calculation results fit well with that results calculated by traditional minimization chisquare method.And its calculation speed,about 1 ms per shot and per spatial point,is about 20 times faster than the minimization chi-square method.Therefore,it is possible for real time feed-back control plasma discharge by electron temperature measured by Thomson scattering on HL-2 M,ITER or CFTER.
Artificial neural network(NN)as a powerful nonlinear data processing method,has been successfully applied to process electron temperature for Thomson scattering system on HL-2 A.A type of perception is chosen.The NN has three layers:input layer,hidden layer,and output layer.Calibration data or measured data are the input layer,hidden layer uses sigmoid function as transfer function,and output layer is electron temperature.The calculation results fit well with that results calculated by traditional minimization chisquare method.And its calculation speed,about 1 ms per shot and per spatial point,is about 20 times faster than the minimization chi-square method.Therefore,it is possible for real time feed-back control plasma discharge by electron temperature measured by Thomson scattering on HL-2 M,ITER or CFTER.
引文
[1]刘春华,黄渊,冯震,等.HL-2A装置YAG激光汤姆逊散射测量电子温度的初步结果[J].强激光与粒子束,2008,20(7):1119-1124.(Liu Chunhua,Huang Yuan,Feng Zhen,et al.Primary results of measuring plasma electron temperature with YAG laser Thomson scattering system on the HL-2ATokamak.High Power Laser and Particle Beams,2008,20(7):1119-1124)
[2]慈佳祥,黄渊,唐昌建,等.HL-2A装置Nd:YAG激光汤姆逊散射多道测量系统[J].强激光与粒子束,2009,21(3):359-364.(Ci Jiaxiang,Huang Yuan,Tang Changjian,et al.Measurement system of multipoint Thomson scattering on HL-2ATokamak.High Power Laser and Particle Beams,2009,21(3):359-364)
[3]Liu C H,Huang Y,Wang Y Q,et al.The progress in development of edge tangential Thomson scattering system on HL-2A[J].Review of Scientific Instruments,2016,87:11E555.
[4]Liu C H,Wang Y Q,Feng Z,et al.Improvement in data processing of Thomson scattering diagnostic on HL-2Atokamak[J].Journal of Instrumentation,2015,10:C12026.
[5]Liu Chunhua,Huang Yuan,Feng Zhen,et al.Electron density measurement by Raman calibration of the HuanLiuqi-2AThomson scattering system[J].Plasma and Fusion Research:Regular Articles,2014,9:1402042.
[6]Rietman E A,Beachy M.A study on failure prediction in a plasma reactor[J].IEEE Trans Semiconductor Manufacturing,1998,11(4):670-680.
[7]Matthew S P.Interpretation of machine-learning-based disruption models for plasma control[J].Plasma Physics and Controlled Fusion,2017,59:085001.
[8]Cannas B,Cau F,Fanni A,et al.Automatic disruption classification at JET:comparison of different pattern recognition techniques[J].Nuclear Fusion,2006,46(7):699-708.
[9]Cannas B,Vries P C,Fanni A,et al.Automatic disruption classification in JET with the ITER-like wall[J].Plasma Physics and Controlled Fusion,2015,57:125003.
[10]Cannas B,Fanni A,Murari A,et al.Automatic disruption classification based on manifold learning for real-time applications on JET[J].Nuclear Fusion,2013,53:093023.
[11]Cannas B,Fanni A,Pautasso G,et al.An adaptive real-time disruption predictor for ASDEX Upgrade[J].Nuclear Fusion,2010,50:075004.
[12]Morabito F C,Versaci M,Pautasso G.Fuzzy-neural approaches to the prediction of disruptions in ASDEX Upgrade[J].Nuclear Fusion,2001,41(11):1715-1724.
[13]Pautasso G.Vries P C,Humphreys D,et al.The ITER disruption mitigation trigger:developing its preliminary design[J].Nuclear Fusion,2018,58:036011.
[14]Wang S Y,Chen Z Y,Huang D W,et al.Prediction of density limit disruptions on the J-TEXT tokamak[J].Plasma Physics and Controlled Fusion,2016,58:055014.
[15]Sengupta A,Ranjan P.Forecasting disruptions in the ADITYA tokamak using neural networks[J].Nuclear Fusion,2000,40(12):1993.
[16]Bishop C M,Roach C M,Hellermann M G.Automatic analysis of JET charge exchange spectra using neural networks[J].Plasma Physics and Controlled Fusion,1993,35:765-773.
[17]Clayton D J,Tritz K,Stutman D,et al.Electron temperature profile reconstructions from multi-energy SXR measurements using neural networks[J].Plasma Physics and Controlled Fusion,2013,55:095015.
[18]Aguiam D E,Silva A,Bobkov V,et al.Implementation of the new multichannel X-mode edge density profile reflectometer for the ICRFantenna on ASDEX Upgrade[J].Review of Scientific Instruments,2016,87:11E722.
[19]Vega J,Murari A,Gonzalez S.A universal support vector machines based method for automatic event location in waveforms and videomovies:Applications to massive nuclear fusion databases[J].Review of Scientific Instruments,2010,81:10E118.
[20]Farias G,Dormido-Canto S,Vega J,et al.Image classification by using a reduced set of features in the TJ-II Thomson scattering diagnostic[J].Fusion Engineering and Design,2018,129:99-103.
[21]Fujii K,Yamada I,Hasuo M.Machine learning of noise in LHD Thomson scattering system[J].Fusion Science and Technology,2018,74(1/2):57-64.
[22]Lee S H,Lee J H,Yamada I,et al.Development of a neural network technique for KSTAR Thomson scattering diagnostics[J].Review of Scientific Instruments,2016,87:11E533.
[23]Greenfield C M,Campbell G L,Carlstrom T N,et al.Real-time digital control,data acquisition,and analysis system for the DIII-D multipulse Thomson scattering diagnostic[J].Rev Sci Instr,1990,61(10):3286-3288.
[24]Narihara K,Yamauchi K,Minami T,et al.Construction of a 100-Hz-repetition-rate 28-channel Thomson scattering system for the JIPPT-IIU Tokamak[J].Japanese Journal of Applied Physics,1996,35:266-273.
[25]姚可,黄渊,冯震,等.最大似然法在HL-2A汤姆逊散射中的初步应用[J].核聚变与等离子体物理,2010,30(3):203-207.(Yao Ke,Huang Yuan,Feng Zhen,et al.Preliminary application of maximum likelihood method in HL-2A Thomson scattering system.Nuclear Fusion and Plasma Physics,2010,30(3):203-207)
[26]Fischer R,Dinklage A,Pasch E.Bayesian modelling of fusion diagnostics[J].Plasma Physics and Controlled Fusion,2003,45(7):1095-1111.
[27]Beurskens M N A,Barth C J,Chu C C,et al.Error analysis of Rijnhuizen Tokamak project Thomson scattering data[J].Review of Scientific Instruments,1999,70:1999-2011.
[2]慈佳祥,黄渊,唐昌建,等.HL-2A装置Nd:YAG激光汤姆逊散射多道测量系统[J].强激光与粒子束,2009,21(3):359-364.(Ci Jiaxiang,Huang Yuan,Tang Changjian,et al.Measurement system of multipoint Thomson scattering on HL-2ATokamak.High Power Laser and Particle Beams,2009,21(3):359-364)
[3]Liu C H,Huang Y,Wang Y Q,et al.The progress in development of edge tangential Thomson scattering system on HL-2A[J].Review of Scientific Instruments,2016,87:11E555.
[4]Liu C H,Wang Y Q,Feng Z,et al.Improvement in data processing of Thomson scattering diagnostic on HL-2Atokamak[J].Journal of Instrumentation,2015,10:C12026.
[5]Liu Chunhua,Huang Yuan,Feng Zhen,et al.Electron density measurement by Raman calibration of the HuanLiuqi-2AThomson scattering system[J].Plasma and Fusion Research:Regular Articles,2014,9:1402042.
[6]Rietman E A,Beachy M.A study on failure prediction in a plasma reactor[J].IEEE Trans Semiconductor Manufacturing,1998,11(4):670-680.
[7]Matthew S P.Interpretation of machine-learning-based disruption models for plasma control[J].Plasma Physics and Controlled Fusion,2017,59:085001.
[8]Cannas B,Cau F,Fanni A,et al.Automatic disruption classification at JET:comparison of different pattern recognition techniques[J].Nuclear Fusion,2006,46(7):699-708.
[9]Cannas B,Vries P C,Fanni A,et al.Automatic disruption classification in JET with the ITER-like wall[J].Plasma Physics and Controlled Fusion,2015,57:125003.
[10]Cannas B,Fanni A,Murari A,et al.Automatic disruption classification based on manifold learning for real-time applications on JET[J].Nuclear Fusion,2013,53:093023.
[11]Cannas B,Fanni A,Pautasso G,et al.An adaptive real-time disruption predictor for ASDEX Upgrade[J].Nuclear Fusion,2010,50:075004.
[12]Morabito F C,Versaci M,Pautasso G.Fuzzy-neural approaches to the prediction of disruptions in ASDEX Upgrade[J].Nuclear Fusion,2001,41(11):1715-1724.
[13]Pautasso G.Vries P C,Humphreys D,et al.The ITER disruption mitigation trigger:developing its preliminary design[J].Nuclear Fusion,2018,58:036011.
[14]Wang S Y,Chen Z Y,Huang D W,et al.Prediction of density limit disruptions on the J-TEXT tokamak[J].Plasma Physics and Controlled Fusion,2016,58:055014.
[15]Sengupta A,Ranjan P.Forecasting disruptions in the ADITYA tokamak using neural networks[J].Nuclear Fusion,2000,40(12):1993.
[16]Bishop C M,Roach C M,Hellermann M G.Automatic analysis of JET charge exchange spectra using neural networks[J].Plasma Physics and Controlled Fusion,1993,35:765-773.
[17]Clayton D J,Tritz K,Stutman D,et al.Electron temperature profile reconstructions from multi-energy SXR measurements using neural networks[J].Plasma Physics and Controlled Fusion,2013,55:095015.
[18]Aguiam D E,Silva A,Bobkov V,et al.Implementation of the new multichannel X-mode edge density profile reflectometer for the ICRFantenna on ASDEX Upgrade[J].Review of Scientific Instruments,2016,87:11E722.
[19]Vega J,Murari A,Gonzalez S.A universal support vector machines based method for automatic event location in waveforms and videomovies:Applications to massive nuclear fusion databases[J].Review of Scientific Instruments,2010,81:10E118.
[20]Farias G,Dormido-Canto S,Vega J,et al.Image classification by using a reduced set of features in the TJ-II Thomson scattering diagnostic[J].Fusion Engineering and Design,2018,129:99-103.
[21]Fujii K,Yamada I,Hasuo M.Machine learning of noise in LHD Thomson scattering system[J].Fusion Science and Technology,2018,74(1/2):57-64.
[22]Lee S H,Lee J H,Yamada I,et al.Development of a neural network technique for KSTAR Thomson scattering diagnostics[J].Review of Scientific Instruments,2016,87:11E533.
[23]Greenfield C M,Campbell G L,Carlstrom T N,et al.Real-time digital control,data acquisition,and analysis system for the DIII-D multipulse Thomson scattering diagnostic[J].Rev Sci Instr,1990,61(10):3286-3288.
[24]Narihara K,Yamauchi K,Minami T,et al.Construction of a 100-Hz-repetition-rate 28-channel Thomson scattering system for the JIPPT-IIU Tokamak[J].Japanese Journal of Applied Physics,1996,35:266-273.
[25]姚可,黄渊,冯震,等.最大似然法在HL-2A汤姆逊散射中的初步应用[J].核聚变与等离子体物理,2010,30(3):203-207.(Yao Ke,Huang Yuan,Feng Zhen,et al.Preliminary application of maximum likelihood method in HL-2A Thomson scattering system.Nuclear Fusion and Plasma Physics,2010,30(3):203-207)
[26]Fischer R,Dinklage A,Pasch E.Bayesian modelling of fusion diagnostics[J].Plasma Physics and Controlled Fusion,2003,45(7):1095-1111.
[27]Beurskens M N A,Barth C J,Chu C C,et al.Error analysis of Rijnhuizen Tokamak project Thomson scattering data[J].Review of Scientific Instruments,1999,70:1999-2011.