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

Huan Xu, Feng Ding, Min Gan, Erfu Yang

Research output: Contribution to journalArticle

Abstract

This paper 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 sub-identification model and derive a two-stage least squares extended stochastic gradient algorithm. In order to improve the parameter estimation accuracy, we employ the multi-innovation identification theory and develop a two-stage least squares multi-innovation extended stochastic gradient algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalInternational Journal of Robust and Nonlinear Control
Publication statusAccepted/In press - 10 Aug 2020

Keywords

  • nonlinear time series
  • parameter estimation
  • decomposition technique
  • multi-innovation identification

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