HL-2A tokamak disruption forecasting based on an artificial neural network

• 来源期刊: 中国物理B（英文版）
• 关键词: disruption prediction artificial neural networks
• 年，卷（期）: 2007,（12）
• 页码范围: 3738-3741
• 页数: 4页
• 语种: 英文

摘要

Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibiliry of developing a neural network predictor that intervenes well in edvance for avoiding plasma disruption or mitigating its effects.