文献类型：期刊文献

中文题名：基于神经网络的托卡马克能量约束时间预测

英文题名：Prediction of Energy Confinement Time in Tokamak Based on Neural Networks

作者：郑书昱[1];彭佳臻[1];张先梅[1];薛二兵[1];虞立敏[1]

机构：[1]华东理工大学理学院,上海200237

年份：2021

卷号：38

期号：4

起止页码：423

中文期刊名：计算物理

外文期刊名：Chinese Journal of Computational Physics

收录：CSTPCD;;Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(11675053,11875131)资助项目。

语种：中文

中文关键词：托卡马克;能量约束时间;神经网络

外文关键词：tokamak;energy confinement time;neural network

摘要：使用机器学习理论中的神经网络方法,根据通用逼近原理对能量约束时间的复杂函数进行逼近,采用托卡马克装置的典型实验数据,设计一种组合结构的神经网络。通过大量的调参试验,给出一套性能最好的参数组合,并与传统幂指数形式的多元线性回归方法进行准确性和数据集迁移能力的比较。结果表明:神经网络模型对于能量约束时间的预测准确率更高,回归性能更好,且具有一定的抗噪声能力,更适合作为能量约束时间的定标或预测工具。
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.