Open AccessAn autoregressive parametric method applied to the autowave process modelling
Author(s) -
Tatiana Gorbunova,
Руслан Иванович Баженов,
Marina Tumanova,
Л. А. Алексеева,
I. Korosteleva
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1679/2/022037
Subject(s) - autoregressive model , parametric statistics , computer science , waveform , process (computing) , parametric model , signal (programming language) , star model , filter (signal processing) , vibration , control theory (sociology) , autoregressive integrated moving average , artificial intelligence , mathematics , time series , physics , acoustics , machine learning , econometrics , statistics , radar , telecommunications , control (management) , computer vision , programming language , operating system
The authors of the paper research the topic of the autoregressive parametric spectral estimation method which is applied to oscillation (vibration). It is illustrated by the study of dynamically stable the combustion modes in a liquid-propellant engine. The scholars suppose a hypothesis that if a model parameter is selected correctly, the method offers a higher resolution. It does not require a dialogue-based weighted signal, and compensated parser/analyzer filter related to the accurate estimates of the oscillating system attributes. The survey proves that parametric spectral estimation amounts to solving an optimization problem with and seeking the autoregressive model value parameters of waveform shaping, in which the model would be as close as possible to the observed signal in reality. Based on the study, the authors design a digital model of the dynamic system that meets all the required properties.