Tokamak plasma large fracture prediction algorithm based on deep neural network

Abstract

The invention belongs to the field of plasma physics, and particularly relates to a Tokamak plasma large fracture prediction algorithm based on a deep neural network, which comprises the following steps: preparing a training data set, performing neural network model creation and prediction calculation, then performing model parameter training, and inputting parameters into a neural network model for calculation after the neural network model training is completed to acquire a real-time fracture possibility value. A customized and optimized neural network model is carried out according to the data characteristics of the fusion device, the model can simply and conveniently access different types of control and diagnosis signals, the problem that a standard neural network model limits a data source is solved, the neural network is more suitable for processing long-sequence, multi-modal and multi-noise-label fusion data. And finally, the effect that the prediction accuracy is 30ms ahead of time and 96.1% of the fracture prediction task is realized.

Description

technical field [0001] The invention belongs to the field of plasma physics, and in particular relates to a deep neural network-based tokamak plasma large rupture prediction algorithm. Background technique [0002] The prior art tokamak plasma large rupture prediction techniques can be roughly divided into two categories: (1) traditional machine learning methods; (2) methods based on standard neural network schemes. [0003] The prediction method based on traditional machine learning obtains a number of low-dimensional physical quantities that are directly related to the rupture through some physical analysis related to the rupture, such as the ratio of density to the Greenwald density limit, the mode-locked amplitude, etc., and then through random forest, Traditional machine learning algorithms such as support vector machines and fully connected neural networks combine various relevant quantities to give the possibility of rupture. The method based on the standard neural n...

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Problems solved by technology

[0006] The purpose of the present invention is to provide a tokamak plasma large rupture prediction algorithm based on a deep neural network, which can solve the limitation of the standard neural network model on the data source and improve the accuracy of prediction

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