基于神经网络的托卡马克能量约束时间预测

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。

Prediction of Energy Confinement Time in Tokamak Based on Neural Networks

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.