Design of HL-2A plasma position predictive model based on deep learning

Abstract

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.