关键词: 鱼骨模, 神经网络, 深度学习, HL-2A装置

Abstract: Based on the deep learning method, a set of fishbone mode (FB) recognition algorithm was developed on the HL-2A tokamak. The algorithm uses 858 shot discharge data (780 for training and 78 for verification) and about 463800 data slices for training and verification, and obtains a convolutional neural network mainly composed of convolutional layer, residual connection layer and fully connected layer. In order to measure the recognition ability of the algorithm, the algorithm was used to scan and identify the 780 discharge data, and a total of 86820 FB slices were identified, of which 4327 were misidentified, and the false alarm rate was 4.75%. In the actual 97145 FB slices, 10325 slices were missed, and the corresponding false negative rate was 10.63%, and the overall recognition accuracy rate reached 94.26%. The false positive rate and false negative rate can meet the accuracy requirements of FB recognition.

Key words: Fishbone, Neural network, Deep learning, HL-2A tokamak