Data filtering-based recursive identification for an exponential autoregressive moving average model by using the multi-innovation theory
This study employs the data filtering technique to investigate the recursive identification problems for a non-linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into...
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| Vydáno v: | IET control theory & applications Ročník 14; číslo 17; s. 2526 - 2534 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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The Institution of Engineering and Technology
26.11.2020
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| ISSN: | 1751-8644, 1751-8652 |
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| Abstract | This study employs the data filtering technique to investigate the recursive identification problems for a non-linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into a filtered identification model and a coloured noise model, then a filtering-based extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, the multi-innovation identification theory is used to develop a filtering-based multi-innovation extended stochastic gradient algorithm for the ExpARMA model. A simulation example is given to demonstrate the superiority of the proposed filtering-based multi-innovation algorithm over the existing algorithms. |
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| AbstractList | This study employs the data filtering technique to investigate the recursive identification problems for a non‐linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into a filtered identification model and a coloured noise model, then a filtering‐based extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, the multi‐innovation identification theory is used to develop a filtering‐based multi‐innovation extended stochastic gradient algorithm for the ExpARMA model. A simulation example is given to demonstrate the superiority of the proposed filtering‐based multi‐innovation algorithm over the existing algorithms. |
| Author | Xu, Huan Ding, Feng Xu, Ling Ma, Fengying Alsaedi, Ahmed Hayat, Tasawar |
| Author_xml | – sequence: 1 givenname: Huan orcidid: 0000-0003-2145-8240 surname: Xu fullname: Xu, Huan organization: 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 2 givenname: Fengying surname: Ma fullname: Ma, Fengying organization: 2School of Electrical Engineering and Automation, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, People's Republic of China – sequence: 3 givenname: Feng orcidid: 0000-0002-2721-2025 surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 4 givenname: Ling surname: Xu fullname: Xu, Ling organization: 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 5 givenname: Ahmed surname: Alsaedi fullname: Alsaedi, Ahmed organization: 3Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia – sequence: 6 givenname: Tasawar surname: Hayat fullname: Hayat, Tasawar organization: 3Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| Keywords | nonlinear exponential autoregressive model linear filter filtering theory average noise recursive estimation coloured noise model autoregressive processes data filtering-based recursive identification filtering-based multiinnovation extended stochastic gradient algorithm gradient methods multiinnovation identification theory ExpARMA model |
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| SubjectTerms | autoregressive processes average noise coloured noise model data filtering‐based recursive identification ExpARMA model filtering theory filtering‐based multiinnovation extended stochastic gradient algorithm gradient methods linear filter multiinnovation identification theory nonlinear exponential autoregressive model recursive estimation Research Article |
| Title | Data filtering-based recursive identification for an exponential autoregressive moving average model by using the multi-innovation theory |
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