Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise

Xu, Huan and Ding, Feng and Yang, Erfu (2020) Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise. IET Control Theory and Applications, 14 (2). pp. 262-270. ISSN 1751-8644

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    Abstract

    This study focuses on the recursive parameter estimation problems for the non-linear exponential autoregressive model with moving average noise (the ExpARMA model for short). By means of the gradient search, an extended stochastic gradient (ESG) algorithm is derived. Considering the difficulty of determining the step-size in the ESG algorithm, a numerical approach is proposed to obtain the optimal step-size. In order to improve the parameter estimation accuracy, the authors employ the multi-innovation identification theory to develop a multi-innovation ESG (MI-ESG) algorithm for the ExpARMA model. Introducing a forgetting factor into the MI-ESG algorithm, the parameter estimation accuracy can be further improved. With an appropriate innovation length and forgetting factor, the variant of the MI-ESG algorithm is effective to identify all the unknown parameters of the ExpARMA model. A simulation example is provided to test the proposed algorithms.

    Creators(s): Xu, Huan, Ding, Feng ORCID logoORCID: https://orcid.org/0000-0002-9787-4171 and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
    Item type:Article
    ID code:72068
    Keywords:ExpARMA model, extended stochastic gradient algorithm, e recursive parameter estimation problems, Electronic computers. Computer science, Control and Systems Engineering, Human-Computer Interaction, Computer Science Applications, Control and Optimization, Electrical and Electronic Engineering
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:16 Apr 2020 13:11
    Last modified:21 Jan 2021 11:51
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/72068
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