In tokamak discharge experiments, the plasma position prediction model’s research is to understand the law of plasma motion and verify the correctness of the plasma position controller design. Although Maxwell equations can completely describe plasma movement, obtaining an accurate physical model for predicting plasma behavior is still challenging. This paper describes a deep neural network model that can accurately predict the HL-2A plasma position. That is a hybrid neural network model based on a long short-term memory network. We introduce the topology, training parameter setting, and prediction result analysis of this model in detail. The test results show that a trained deep neural network model has high prediction accuracy for plasma vertical and horizontal displacements.

在托卡马克放电实验中，等离子体位置预测模型的研究旨在了解等离子体运动的规律，并验证等离子体位置控制器设计的正确性。尽管麦克斯韦方程可以完全描述等离子体的运动，但是获得准确的物理模型来预测等离子体的行为仍然具有挑战性。本文介绍了一种可以准确预测HL-2A血浆位置的深度神经网络模型。那是一个基于长期短期记忆网络的混合神经网络模型。我们详细介绍了该模型的拓扑，训练参数设置和预测结果分析。测试结果表明，经过训练的深度神经网络模型对等离子体的垂直和水平位移具有较高的预测精度。