# Recursive identification of Wiener systems

International Journal of Applied Mathematics and Computer Science (2001)

- Volume: 11, Issue: 4, page 977-991
- ISSN: 1641-876X

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topGreblicki, Włodzimierz. "Recursive identification of Wiener systems." International Journal of Applied Mathematics and Computer Science 11.4 (2001): 977-991. <http://eudml.org/doc/207541>.

@article{Greblicki2001,

abstract = {A Wiener system, i.e. a cascade system consisting of a linear dynamic subsystem and a nonlinear memoryless subsystem is identified. The a priori information is nonparametric, i.e. neither the functional form of the nonlinear characteristic nor the order of the dynamic part are known. Both the input signal and the disturbance are Gaussian white random processes. Recursive algorithms to estimate the nonlinear characteristic are proposed and their convergence is shown. Results of numerical simulation are also given. A known algorithm recovering the impulse response of the dynamic part is presented in a recursive form.},

author = {Greblicki, Włodzimierz},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {recursive identification; system identification; nonparametric identification; Wiener system; non-parametric approach; recursive algorithms; identification; discrete-time Wiener systems; static nonlinearity; pointwise convergence in probability; convergence rates},

language = {eng},

number = {4},

pages = {977-991},

title = {Recursive identification of Wiener systems},

url = {http://eudml.org/doc/207541},

volume = {11},

year = {2001},

}

TY - JOUR

AU - Greblicki, Włodzimierz

TI - Recursive identification of Wiener systems

JO - International Journal of Applied Mathematics and Computer Science

PY - 2001

VL - 11

IS - 4

SP - 977

EP - 991

AB - A Wiener system, i.e. a cascade system consisting of a linear dynamic subsystem and a nonlinear memoryless subsystem is identified. The a priori information is nonparametric, i.e. neither the functional form of the nonlinear characteristic nor the order of the dynamic part are known. Both the input signal and the disturbance are Gaussian white random processes. Recursive algorithms to estimate the nonlinear characteristic are proposed and their convergence is shown. Results of numerical simulation are also given. A known algorithm recovering the impulse response of the dynamic part is presented in a recursive form.

LA - eng

KW - recursive identification; system identification; nonparametric identification; Wiener system; non-parametric approach; recursive algorithms; identification; discrete-time Wiener systems; static nonlinearity; pointwise convergence in probability; convergence rates

UR - http://eudml.org/doc/207541

ER -

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