Artificial neural network approach applied to data processing of Thomson scattering on HL-2A

Liu Chunhua; Hou Zhipei; Wang Yuqin; Feng Zhen; Xia Fan; Huang Yuan

doi:10.11884/HPLPB201931.180206

Volume 31 Issue 2

Feb. 2019

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Liu Chunhua, Hou Zhipei, Wang Yuqin, et al. Artificial neural network approach applied to data processing of Thomson scattering on HL-2A[J]. High Power Laser and Particle Beams, 2019, 31: 022003. doi: 10.11884/HPLPB201931.180206

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Liu Chunhua, Hou Zhipei, Wang Yuqin, et al. Artificial neural network approach applied to data processing of Thomson scattering on HL-2A[J]. High Power Laser and Particle Beams, 2019, 31: 022003. doi: 10.11884/HPLPB201931.180206

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Artificial neural network approach applied to data processing of Thomson scattering on HL-2A

doi: 10.11884/HPLPB201931.180206

Center for Fusion Science, Southwestern Institute of Physics, Chengdu 610041, China

Received Date: 2018-07-28
Rev Recd Date: 2019-01-28
Publish Date: 2019-02-15

Abstract

Abstract

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-2A. 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 chi-square 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-2M, ITER or CFTER.
- Thomson scattering,
- neural network,
- data processing,
- electron temperature,
- HL-2A Tokamak

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References(27)

References

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