Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative sear...

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Vydáno v:IET control theory & applications Ročník 13; číslo 18; s. 3040 - 3051
Hlavní autoři: Ma, Hao, Pan, Jian, Ding, Feng, Xu, Ling, Ding, Wenfang
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Institution of Engineering and Technology 17.12.2019
Témata:
autoregressive moving average noise
autoregressive moving average processes
gradient methods
identification problem
information vector
iterative methods
iterative parameter estimation
iterative search
least squares approximations
multivariable output‐error‐like autoregressive moving average systems
multivariable output‐error‐like system
parameter estimation
parameter vector
recursive least squares algorithm
redundant parameter estimates
Research Article
unknown variables
vectors
iterative methods
least squares approximations
autoregressive moving average processes
multivariable output-error-like autoregressive moving average systems
recursive least squares algorithm
redundant parameter estimates
vectors
autoregressive moving average noise
parameter vector
iterative search
information vector
identification problem
parameter estimation
multivariable output-error-like system
iterative parameter estimation
unknown variables
gradient methods
ISSN:1751-8644, 1751-8652
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Shrnutí:This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2019.0112