Using neural networks with data quantization for time series analysis in LHC superconducting magnets - Wielgosz, Maciej; Skoczeń, Andrzej (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: Large Hadron Collider; LSTM architecture; signal modelling