A fast nonlinear model identification method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem r...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on automatic control Ročník 50; číslo 8; s. 1211 - 1216
Hlavní autoři: Kang Li, Jian-Xun Peng, Irwin, G.W.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.08.2005
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Applied sciences
Computational complexity
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
fast recursive algorithm
Least squares methods
Matrix decomposition
Modelling and identification
Nonlinear dynamical systems
nonlinear system identification
Nonlinear systems
Numerical stability
Parameter estimation
System identification
US Department of Transportation
nonlinear system identification
Model matching
Identification
System identification
Dynamical system
Fast algorithm
Non linear system
Recursive algorithm
Computational complexity
fast recursive algorithm
Numerical stability
ISSN:0018-9286, 1558-2523
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
content type line 23
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2005.852557