Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering

This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational e...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Nonlinear dynamics Ročník 84; číslo 2; s. 1045 - 1053
Hlavní autoři: Wang, Yanjiao, Ding, Feng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.04.2016
Springer Nature B.V
Témata:
Algorithms
Automotive Engineering
Classical Mechanics
Computational efficiency
Computer simulation
Computing time
Control
Dynamical Systems
Economic models
Engineering
Estimating techniques
Filtering
Filtration
Least squares
Least squares method
Mechanical Engineering
Nonlinear dynamics
Original Paper
Parameter estimation
Recursive
System effectiveness
Vibration
Gradient search
Parameter estimation
Nonlinear system
Least squares
Data filtering
ISSN:0924-090X, 1573-269X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the data filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-015-2548-5