Joint two‐stage multi‐innovation recursive least squares parameter and fractional‐order estimation algorithm for the fractional‐order input nonlinear output‐error autoregressive model

Summary This paper mainly investigates the issue of parameter identification for the fractional‐order input nonlinear output error autoregressive (IN‐OEAR) model. In order to avoid the problem of large computation of redundant parameter estimation, the output form of the system can be expressed by a...

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Published in:	International journal of adaptive control and signal processing Vol. 37; no. 7; pp. 1650 - 1670
Main Authors:	Hu, Chong, Ji, Yan, Ma, Caiqing
Format:	Journal Article
Language:	English
Published:	Bognor Regis Wiley Subscription Services, Inc 01.07.2023
Subjects:	Algorithms Autoregressive models Error analysis fractional‐order model gradient search hierarchical identification Identification methods Innovations key term separation Least squares multi‐innovation theory Parameter estimation Parameter identification
ISSN:	0890-6327, 1099-1115
Online Access:	Get full text
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Abstract	Summary This paper mainly investigates the issue of parameter identification for the fractional‐order input nonlinear output error autoregressive (IN‐OEAR) model. In order to avoid the problem of large computation of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the key term separation. Through employing the hierarchial identification principle, the fractional‐order IN‐OEAR model is decomposed into two sub‐models with a smaller number of parameters. On the basis of the recursive identification methods, a recursive least squares sub‐algorithm and a gradient stochastic sub‐algorithm are proposed to estimate the parameters and the fractional‐order, respectively. With the aim of achieving more accurate parameter estimates, a two‐stage multi‐innovation least recursive algorithm is proposed by means of the multi‐innovation identification theory. The numerical simulation results test the effectiveness of the proposed methods.
AbstractList	This paper mainly investigates the issue of parameter identification for the fractional‐order input nonlinear output error autoregressive (IN‐OEAR) model. In order to avoid the problem of large computation of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the key term separation. Through employing the hierarchial identification principle, the fractional‐order IN‐OEAR model is decomposed into two sub‐models with a smaller number of parameters. On the basis of the recursive identification methods, a recursive least squares sub‐algorithm and a gradient stochastic sub‐algorithm are proposed to estimate the parameters and the fractional‐order, respectively. With the aim of achieving more accurate parameter estimates, a two‐stage multi‐innovation least recursive algorithm is proposed by means of the multi‐innovation identification theory. The numerical simulation results test the effectiveness of the proposed methods. Summary This paper mainly investigates the issue of parameter identification for the fractional‐order input nonlinear output error autoregressive (IN‐OEAR) model. In order to avoid the problem of large computation of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the key term separation. Through employing the hierarchial identification principle, the fractional‐order IN‐OEAR model is decomposed into two sub‐models with a smaller number of parameters. On the basis of the recursive identification methods, a recursive least squares sub‐algorithm and a gradient stochastic sub‐algorithm are proposed to estimate the parameters and the fractional‐order, respectively. With the aim of achieving more accurate parameter estimates, a two‐stage multi‐innovation least recursive algorithm is proposed by means of the multi‐innovation identification theory. The numerical simulation results test the effectiveness of the proposed methods.
Author	Hu, Chong Ji, Yan Ma, Caiqing
Author_xml	– sequence: 1 givenname: Chong surname: Hu fullname: Hu, Chong organization: Qingdao University of Science and Technology – sequence: 2 givenname: Yan orcidid: 0000-0002-3245-2437 surname: Ji fullname: Ji, Yan email: yjichina@163.com organization: Qingdao University of Science and Technology – sequence: 3 givenname: Caiqing surname: Ma fullname: Ma, Caiqing organization: Qingdao University of Science and Technology
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Snippet	Summary This paper mainly investigates the issue of parameter identification for the fractional‐order input nonlinear output error autoregressive (IN‐OEAR)... This paper mainly investigates the issue of parameter identification for the fractional‐order input nonlinear output error autoregressive (IN‐OEAR) model. In...
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SubjectTerms	Algorithms Autoregressive models Error analysis fractional‐order model gradient search hierarchical identification Identification methods Innovations key term separation Least squares multi‐innovation theory Parameter estimation Parameter identification
Title	Joint two‐stage multi‐innovation recursive least squares parameter and fractional‐order estimation algorithm for the fractional‐order input nonlinear output‐error autoregressive model
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