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...

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Bibliographic Details
Published in:Journal of the Franklin Institute Vol. 353; no. 7; pp. 1518 - 1526
Main Authors: Ding, Feng, Liu, Ximei, Liu, Manman
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2016
Subjects:
Dynamical systems
Least squares method
Mathematical analysis
Mathematical models
Nonlinear dynamics
Nonlinearity
Recursive
Vectors (mathematics)
ISSN:0016-0032, 1879-2693
Online Access:Get full text
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Summary: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.
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ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2016.02.013