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Two‐stage recursive identification algorithms for a class of nonlinear time series models with colored noise
Author(s) -
Xu Huan,
Ding Feng,
Gan Min,
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.5206
Subject(s) - autoregressive model , algorithm , series (stratigraphy) , identification (biology) , nonlinear system , noise (video) , computer science , exponential function , class (philosophy) , colored , least squares function approximation , estimation theory , autoregressive–moving average model , time series , mathematics , artificial intelligence , machine learning , statistics , mathematical analysis , estimator , paleontology , botany , physics , materials science , composite material , quantum mechanics , image (mathematics) , biology
Summary This article concentrates on the recursive identification algorithms for the exponential autoregressive model with moving average noise. Using the decomposition technique, we transform the original identification model into a linear and nonlinear subidentification model and derive a two‐stage least squares (LS) extended stochastic gradient (ESG) algorithm. In order to improve the parameter estimation accuracy, we employ the multi‐innovation identification theory and develop a two‐stage LS multi‐innovation ESG algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms.