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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Veröffentlicht in:International journal of adaptive control and signal processing Jg. 37; H. 7; S. 1650 - 1670
Hauptverfasser: Hu, Chong, Ji, Yan, Ma, Caiqing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.07.2023
Schlagworte:
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
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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
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  surname: Ji
  fullname: Ji, Yan
  email: yjichina@163.com
  organization: Qingdao University of Science and Technology
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  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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https://www.proquest.com/docview/2832169300
Volume 37
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