Prediction of Energy Confinement Time in Tokamak Based on Neural Networks

doi:10.19596/j.cnki.1001-246x.8277

Abstract

Abstract:

A neural network method in machine learning theory is used to approximate complex function of energy confinement time in tokamak according to the general approximation principle. A combined structure neural network is designed based on typical experimental data of domestic tokamak. Though a series of parameter adjustment tests, a set of parameters with best performance is obtained. Energy confinement times calculated with neural network model are compared with those by power exponential multiple linear regression. It shows that the neural network model has better accuracy and prediction performance. Noise resistance experiments show that the neural network model has certain anti-noise ability. Therefore, neural network is a favorable candidate for calibration or prediction of energy confinement time.

Key words: tokamak, energy confinement time, neural network

CLC Number:

O532⁺.1

Shuyu ZHENG, Jiazhen PENG, Xianmei ZHANG, Erbing XUE, Limin YU. Prediction of Energy Confinement Time in Tokamak Based on Neural Networks[J]. Chinese Journal of Computational Physics, 2021, 38(4): 423-430.

Figures/Tables 10

Fig.1 Experimental plasma current during steady state of discharge

Fig.2 Structure of a multilayer perception

Fig.3 Structure of a composite network

Fig.4 Residuals of predicted values of composite networks and shallow networks

Table 1 Values of parameters in the network

学习率	优化器	学习率衰减	Batch size	正则化系数
3×10^-4	ADAM(0.9, 0.96)	0.994	0.235	0.002

Table 2 Performance of multiple regression model and neural network model

	e_RMSE	e_MAPE/%	R²
多元线性回归	0.011	17.104	0.335
神经网络模型	0.007	12.194	0.729

Fig.5 Energy confinement times with multiple regression model and neural network model in total sets

Table 3 Performance of multiple regression model and neural network model in testing sets

	e_RMSE	e_MAPE/%	R²
多元线性回归	0.022	20.869	0.149
神经网络模型	0.010	13.518	0.574

Fig.6 Energy confinement times with multiple regression model and neural network model in testing sets

Table 4 Resistance to noise of the neural network model

噪声数据比例/%	e_RMSE	e_MAPE/%	R²
0	0.010	13.518	0.574
5	0.013	13.741	0.562
10	0.013	13.852	0.519

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