Hierarchical least squares based iterative estimation algorithm for multivariable Box–Jenkins-like systems using the auxiliary model

This paper presents a hierarchical least squares iterative algorithm to estimate the parameters of multivariable Box–Jenkins-like systems by combining the hierarchical identification principle and the auxiliary model identification idea. The key is to decompose a multivariable systems into two subsy...

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Vydané v:Applied mathematics and computation Ročník 218; číslo 9; s. 5580 - 5587
Hlavní autori: Zhang, Zhening, Jia, Jie, Ding, Ruifeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 2012
Predmet:
Algorithms
Auxiliary model identification
Estimates
Hierarchical identification
Least squares
Least squares method
Mathematical analysis
Mathematical models
Multivariable
Multivariable Box–Jenkins-like model
Noise
Parameter estimation
System modelling
Vectors (mathematics)
Parameter estimation
Auxiliary model identification
Multivariable Box–Jenkins-like model
System modelling
Hierarchical identification
Least squares
ISSN:0096-3003, 1873-5649
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Shrnutí:This paper presents a hierarchical least squares iterative algorithm to estimate the parameters of multivariable Box–Jenkins-like systems by combining the hierarchical identification principle and the auxiliary model identification idea. The key is to decompose a multivariable systems into two subsystems by using the hierarchical identification principle. As there exist the unmeasurable noise-free outputs and noise terms in the information vector, the solution is using the auxiliary model identification idea to replace the unmeasurable variables with the outputs of the auxiliary model and the estimated residuals. A numerical example is given to show the performance of the proposed algorithm.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2011.11.051