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

Huan Xu, Feng Ding, Min Gan, Erfu Yang

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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


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

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