Prediction of density limit disruption boundaries from diagnostic signals using neural networks
Prediction of density limit disruption boundaries from diagnostic signals using neural networks
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DOI：
10.1088/0029-5515/41/5/302
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发表时间：
2001-05
期刊：
影响因子：
3.3
通讯作者：
A. Sengupta;P. Ranjan
中科院分区：
文献类型：
--
作者：
A. Sengupta;P. Ranjan
尝试使用神经网络对密度极限破坏情况下的破坏边界进行预测。使用实验信号作为输入，从长远来看，网络应该能够向真实的时间控制系统提供关于放电可能中断的密度极限的信息，以便密度可以保持在该极限以下。使用来自ADITYA托卡马克的几个诊断信号，并在选定的时刻将其呈现给神经网络输入，以便在这些时刻中的每个时刻预测密度边界。为了检查使用网络作为真实的时间中断警报的可能性，已经建立了中断阈值。对于大多数放电来说，这个阈值在实际中断之前很久就达到了。神经网络还用于对特定诊断集进行优化，以获得对于预测密度极限最关键的诊断集。优化结果具有Murakami和Hugill标度律的某些特征。优化后的网络与原来的网络相比，效果很好。
An attempt is made to make a prediction of the disruption boundaries for the density limit disruption case using a neural network. Using experimental signals as input, the network should, in the long run, be able to provide information to the real time control systems about the density limit at which a discharge is likely to disrupt, so that the density can be kept below that limit. Several diagnostic signals are used from the ADITYA tokamak and are presented at selected time instants to the neural network inputs, in order to predict, at each of these instants, the density boundary. A disruption threshold has been established in order to examine the possibility of using the network as a real time disruption alarm. For most of the discharges this threshold is reached much before the actual disruption. The neural network is also used to make an optimization of the particular set of diagnostics in order to obtain the ones most crucial for predicting the density limit. The results of optimization have some of the features of the scaling laws of Murakami and Hugill. The optimized network compares well with the original one.