期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2019
卷号：29
期号：3
页码：1-13
DOI：10.2478/amcs-2019-0037
出版社：De Gruyter Open
摘要：The aim of this paper is to present a model based on the recurrent neural network (RNN) architecture, the
long short-term memory (LSTM) in particular, for modeling the work parameters of Large Hadron Collider (LHC) superconducting
magnets. High-resolution data available in the post mortem database were used to train a set of models and compare
their performance for various hyper-parameters such as input data quantization and the number of cells. A novel approach
to signal level quantization allowed reducing the size of the model, simplifying the tuning of the magnet monitoring
system and making the process scalable. The paper shows that an RNN such as the LSTM or a gated recurrent unit (GRU)
can be used for modeling high-resolution signals with the accuracy of over 0.95 and a small number of parameters, ranging
from 800 to 1200. This makes the solution suitable for hardware implementation, which is essential in the case of
monitoring the performance critical and high-speed signal of LHC superconducting magnets.
关键词：Large Hadron Collider; LSTM architecture; signal modelling;
