Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

This article investigates the parameter identification problems of the stochastic systems described by the input‐nonlinear output‐error (IN‐OE) model. This IN‐OE model consists of two submodels, one is an input nonlinear model and the other is a linear output‐error model. The difficulty in the param...

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Vydané v:International journal of robust and nonlinear control Ročník 34; číslo 2; s. 1120 - 1147
Hlavní autori: Ding, Feng, Xu, Ling, Zhang, Xiao, Ma, Hao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 25.01.2024
Predmet:
auxiliary model
Error analysis
hierarchical identification
Iterative algorithms
Iterative methods
least squares
Mathematical models
nonlinear system
over‐parameterization
Parameter identification
Parameterization
Stochastic systems
System effectiveness
ISSN:1049-8923, 1099-1239
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Shrnutí:This article investigates the parameter identification problems of the stochastic systems described by the input‐nonlinear output‐error (IN‐OE) model. This IN‐OE model consists of two submodels, one is an input nonlinear model and the other is a linear output‐error model. The difficulty in the parameter identification of the IN‐OE model is that the information vector contains the unknown variables, which are the noise‐free (true) outputs of the system, the approach taken here is to replace the unknown terms with the outputs of the auxiliary model. Based on the over‐parameterization model and the hierarchical identification principle, an over‐parameterization auxiliary model hierarchical gradient‐based iterative algorithm and an over‐parameterization auxiliary model hierarchical least‐squares‐based iterative algorithm are proposed to estimate the unknown parameters of the IN‐OE systems. Finally, two numerical simulation examples are given to demonstrate the effectiveness of the proposed algorithms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7014