Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise

This paper considers the parameter estimation of a multiple-input–output-error system with autoregressive noise. In order to solve the problem of the information vector containing unknown inner variables, an auxiliary model-based recursive generalized least squares algorithm and a least squares-base...

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Bibliographic Details
Published in:Circuits, systems, and signal processing Vol. 37; no. 5; pp. 1884 - 1906
Main Author: Ding, Jiling
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2018
Springer Nature B.V
Subjects:
Algorithms
Circuits and Systems
Computer simulation
Economic models
Electrical Engineering
Electronics and Microelectronics
Engineering
Estimating techniques
Instrumentation
Iterative algorithms
Iterative methods
Least squares
Mathematical models
Parameter estimation
Signal,Image and Speech Processing
Iterative algorithm
Parameter estimation
Auxiliary model
Multivariable system
Least squares
ISSN:0278-081X, 1531-5878
Online Access:Get full text
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Summary:This paper considers the parameter estimation of a multiple-input–output-error system with autoregressive noise. In order to solve the problem of the information vector containing unknown inner variables, an auxiliary model-based recursive generalized least squares algorithm and a least squares-based iterative algorithm are proposed according to the auxiliary model identification idea and the iterative search principle. The simulation results indicate that the least squares-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model-based recursive generalized least squares algorithm. Two examples are given to test the proposed algorithms.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-017-0636-0