The recursive least squares identification algorithm for a class of Wiener nonlinear systems

Many physical systems can be modeled by a Wiener nonlinear model, which consists of a linear dynamic system followed by a nonlinear static function. This work is concerned with the identification of Wiener systems whose output nonlinear function is assumed to be continuous and invertible. A recursiv...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of the Franklin Institute Ročník 353; číslo 7; s. 1518 - 1526
Hlavní autoři: Ding, Feng, Liu, Ximei, Liu, Manman
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2016
Témata:
Dynamical systems
Least squares method
Mathematical analysis
Mathematical models
Nonlinear dynamics
Nonlinearity
Recursive
Vectors (mathematics)
ISSN:0016-0032, 1879-2693
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Many physical systems can be modeled by a Wiener nonlinear model, which consists of a linear dynamic system followed by a nonlinear static function. This work is concerned with the identification of Wiener systems whose output nonlinear function is assumed to be continuous and invertible. A recursive least squares algorithm is presented based on the auxiliary model identification idea. To solve the difficulty of the information vector including the unmeasurable variables, the unknown terms in the information vector are replaced with their estimates, which are computed through the preceding parameter estimates. Finally, an example is given to support the proposed method.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2016.02.013