Two‐stage recursive identification algorithms for a class of nonlinear time series models with colored noise | Zendy

Xu Huan | Zendy; Ding Feng | Zendy; Gan Min | Zendy; Yang Erfu | Zendy

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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.

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