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

Xu, Huan and Ding, Feng and Gan, Min and Yang, Erfu (2020) Two-stage recursive identification algorithms for a class of nonlinear time series models with colored noise. International Journal of Robust and Nonlinear Control, 30 (17). pp. 7766-7782. ISSN 1049-8923 (https://doi.org/10.1002/rnc.5206)

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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.

ORCID iDs

Xu, Huan, Ding, Feng, Gan, Min and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
  • Item type:Article
    ID code:73615
    Dates:
    Date
    Event
    25 November 2020
    Published
    10 August 2020
    Accepted
    Keywords:nonlinear time series, parameter estimation, decomposition technique, multi-innovation identification, Manufactures, Biomedical Engineering, Chemical Engineering(all), Mechanical Engineering, Industrial and Manufacturing Engineering, Control and Systems Engineering, Electrical and Electronic Engineering, Aerospace Engineering
    Subjects:Technology > Manufactures
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:14 Aug 2020 08:51
    Last modified:27 Aug 2023 05:15
    URI:https://strathprints.strath.ac.uk/id/eprint/73615
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