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 (https://doi.org/10.1049/iet-cta.2019.0429)
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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.
ORCID iDs
Xu, Huan, Ding, Feng and Yang, Erfu ORCID: https://orcid.org/0000-0003-1813-5950;-
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Item type: Article ID code: 72068 Dates: DateEvent29 January 2020Published9 October 2019Published Online8 October 2019AcceptedSubjects: 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: 10 Sep 2024 18:30 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/72068