Auxiliary Model Maximum Likelihood Moving‐Data‐Window Generalized Extended Gradient‐Based Iterative Algorithm for Multivariable Autoregressive Output‐Error Autoregressive Moving‐Average Systems

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Main Authors: Qian Zhang, Ximei Liu
Format: Artículo Open Access
Published: Wiley 2025
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author Qian Zhang
Ximei Liu
author_facet Qian Zhang
Ximei Liu
Qian Zhang
Ximei Liu
collection Wiley Open Access
contents Auxiliary Model Maximum Likelihood Moving‐Data‐Window Generalized Extended Gradient‐Based Iterative Algorithm for Multivariable Autoregressive Output‐Error Autoregressive Moving‐Average Systems Qian Zhang Ximei Liu Optimal Control Applications and Methods ABSTRACTThis article considers the parameter estimation problems of multivariable autoregressive output‐error autoregressive moving‐average systems. To alleviate the identification difficulty of systems, we decompose the multivariable autoregressive output‐error autoregressive moving‐average system into several subsystems. To reduce the impact of colored noises on parameter estimation accuracy, we propose an auxiliary model maximum likelihood generalized extended gradient‐based iterative algorithm using the auxiliary model idea and the maximum likelihood principle. In addition, we use the multi‐innovation identification theory to introduce a moving data window and propose an auxiliary model maximum likelihood moving‐data‐window generalized extended gradient‐based iterative algorithm. These two proposed algorithms produce more accurate parameter estimates than the existing auxiliary model generalized extended gradient‐based iterative algorithm. Simulation examples illustrate the effectiveness of the proposed algorithms. 10.1002/oca.3321 http://onlinelibrary.wiley.com/termsAndConditions#vor
doi_str_mv 10.1002/oca.3321
format Artículo Open Access
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institution Wiley Open Access
license_str_mv http://onlinelibrary.wiley.com/termsAndConditions#vor
publishDate 2025
publisher Wiley
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spellingShingle Auxiliary Model Maximum Likelihood Moving‐Data‐Window Generalized Extended Gradient‐Based Iterative Algorithm for Multivariable Autoregressive Output‐Error Autoregressive Moving‐Average Systems
Qian Zhang
Ximei Liu
Optimal Control Applications and Methods
Auxiliary Model Maximum Likelihood Moving‐Data‐Window Generalized Extended Gradient‐Based Iterative Algorithm for Multivariable Autoregressive Output‐Error Autoregressive Moving‐Average Systems Qian Zhang Ximei Liu Optimal Control Applications and Methods ABSTRACTThis article considers the parameter estimation problems of multivariable autoregressive output‐error autoregressive moving‐average systems. To alleviate the identification difficulty of systems, we decompose the multivariable autoregressive output‐error autoregressive moving‐average system into several subsystems. To reduce the impact of colored noises on parameter estimation accuracy, we propose an auxiliary model maximum likelihood generalized extended gradient‐based iterative algorithm using the auxiliary model idea and the maximum likelihood principle. In addition, we use the multi‐innovation identification theory to introduce a moving data window and propose an auxiliary model maximum likelihood moving‐data‐window generalized extended gradient‐based iterative algorithm. These two proposed algorithms produce more accurate parameter estimates than the existing auxiliary model generalized extended gradient‐based iterative algorithm. Simulation examples illustrate the effectiveness of the proposed algorithms. 10.1002/oca.3321 http://onlinelibrary.wiley.com/termsAndConditions#vor
title Auxiliary Model Maximum Likelihood Moving‐Data‐Window Generalized Extended Gradient‐Based Iterative Algorithm for Multivariable Autoregressive Output‐Error Autoregressive Moving‐Average Systems
topic Optimal Control Applications and Methods
url https://onlinelibrary.wiley.com/doi/10.1002/oca.3321