Estimation of plasma equilibrium parameters via a neural network approach

doi:10.1088/1674-1056/ab55d1

Abstract Plasma equilibrium parameters such as position, X-point, internal inductance, and poloidal beta are essential information for efficient and safe operation of tokamak. In this work, the artificial neural network is used to establish a non-linear relationship between the measured diagnostic signals and selected equilibrium parameters. The estimation process is split into a preliminary classification of the kind of equilibrium (limiter or divertor) and subsequent inference of the equilibrium parameters. The training and testing datasets are generated by the tokamak simulation code (TSC), which has been benchmarked with the EAST experimental data. The noise immunity of the inference model is tested. Adding noise to model inputs during training process is proved to have a certain ability for maintaining performance.

Keywords: neural network fusion plasma equilibrium noise immunity

Received: 25 August 2019
Revised: 22 October 2019
Accepted manuscript online:

PACS:	52.55.Fa	(Tokamaks, spherical tokamaks)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	52.70.-m	(Plasma diagnostic techniques and instrumentation)
	52.25.Xz	(Magnetized plasmas)

Fund: Project supported by the National Magnetic Confinement Fusion Energy R&D Program of China (Grant No. 2018YFE0302100), the National Key Research and Development Program of China (Grant Nos. 2017YFE0300500 and 2017YFE0300501), the National Natural Science Foundation of China (Grant Nos. 11575245, 11805236, and 11905256), and Young and Middle-aged Academic Back-bone Finance Fund from Anhui Medical University.

Corresponding Authors: Fei Yang
E-mail: yangfei@ahmu.edu.cn

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Zi-Jian Zhu(朱子健), Yong Guo(郭勇), Fei Yang(杨飞), Bing-Jia Xiao(肖炳甲), Jian-Gang Li(李建刚) Estimation of plasma equilibrium parameters via a neural network approach 2019 Chin. Phys. B 28 125204

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Estimation of plasma equilibrium parameters via a neural network approach

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