Premium
Three‐stage multi‐innovation parameter estimation for an exponential autoregressive time‐series model with moving average noise by using the data filtering technique
Author(s) -
Xu Huan,
Ding Feng,
Yang Erfu
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5267
Subject(s) - autoregressive model , series (stratigraphy) , identification (biology) , autoregressive–moving average model , noise (video) , estimation theory , computer science , exponential function , moving average , time series , algorithm , star model , system identification , mathematics , artificial intelligence , autoregressive integrated moving average , data modeling , machine learning , statistics , paleontology , mathematical analysis , botany , database , image (mathematics) , biology , computer vision
Summary This paper studies the data filtering‐based identification algorithms for an exponential autoregressive time‐series model with moving average noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into three sub‐identification (Sub‐ID) models, and a filtering‐based three‐stage extended stochastic gradient algorithm is derived for identifying these Sub‐ID models. In order to improve the parameter estimation accuracy, a filtering‐based three‐stage multi‐innovation extended stochastic gradient (F‐3S‐MIESG) algorithm is developed by using the multi‐innovation identification theory. The simulation results indicate that the proposed F‐3S‐MIESG algorithm can work well.