Semirecursive nonparametric algorithms for Hammerstein systems with stochastic autocorrelated input

Ly Inn Chung; Tsair Chuan Lin; Chun Chao Wang

doi:10.1080/00207721.2021.2010833

Semirecursive nonparametric algorithms for Hammerstein systems with stochastic autocorrelated input

Ly Inn Chung, Tsair Chuan Lin, Chun Chao Wang^*

^*Corresponding author for this work

National Taipei University

Research output: Contribution to journal › Journal Article › peer-review

1 Scopus citations

Abstract

A Hammerstein system comprises a nonlinear static subsystem and a linear dynamic subsystem. Herein, semirecursive nonparametric estimators are proposed for the nonlinear static subsystem, and its asymptotic unbiasedness and consistency properties are demonstrated. The estimators are competitive in terms of computational cost and data storage capacity. The performance of the proposed algorithms was examined through both Monte Carlo simulation and application to empirical data.

Original language	English
Pages (from-to)	1503-1515
Number of pages	13
Journal	International Journal of Systems Science
Volume	53
Issue number	7
DOIs	https://doi.org/10.1080/00207721.2021.2010833
State	Published - 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

ARX
Semirecursive
asymptotic unbiasedness
consistency
nonlinearity
two-stage optimisation

Access to Document

10.1080/00207721.2021.2010833

Cite this

@article{167d13cfab2e41938a3a9c6875035b26,

title = "Semirecursive nonparametric algorithms for Hammerstein systems with stochastic autocorrelated input",

abstract = "A Hammerstein system comprises a nonlinear static subsystem and a linear dynamic subsystem. Herein, semirecursive nonparametric estimators are proposed for the nonlinear static subsystem, and its asymptotic unbiasedness and consistency properties are demonstrated. The estimators are competitive in terms of computational cost and data storage capacity. The performance of the proposed algorithms was examined through both Monte Carlo simulation and application to empirical data.",

keywords = "ARX, Semirecursive, asymptotic unbiasedness, consistency, nonlinearity, two-stage optimisation",

author = "Chung, {Ly Inn} and Lin, {Tsair Chuan} and Wang, {Chun Chao}",

note = "Publisher Copyright: {\textcopyright} 2021 Informa UK Limited, trading as Taylor & Francis Group.",

year = "2022",

doi = "10.1080/00207721.2021.2010833",

language = "英语",

volume = "53",

pages = "1503--1515",

journal = "International Journal of Systems Science",

issn = "0020-7721",

publisher = "Taylor and Francis Ltd.",

number = "7",

}

TY - JOUR

T1 - Semirecursive nonparametric algorithms for Hammerstein systems with stochastic autocorrelated input

AU - Chung, Ly Inn

AU - Lin, Tsair Chuan

AU - Wang, Chun Chao

PY - 2022

Y1 - 2022

N2 - A Hammerstein system comprises a nonlinear static subsystem and a linear dynamic subsystem. Herein, semirecursive nonparametric estimators are proposed for the nonlinear static subsystem, and its asymptotic unbiasedness and consistency properties are demonstrated. The estimators are competitive in terms of computational cost and data storage capacity. The performance of the proposed algorithms was examined through both Monte Carlo simulation and application to empirical data.

AB - A Hammerstein system comprises a nonlinear static subsystem and a linear dynamic subsystem. Herein, semirecursive nonparametric estimators are proposed for the nonlinear static subsystem, and its asymptotic unbiasedness and consistency properties are demonstrated. The estimators are competitive in terms of computational cost and data storage capacity. The performance of the proposed algorithms was examined through both Monte Carlo simulation and application to empirical data.

KW - ARX

KW - Semirecursive

KW - asymptotic unbiasedness

KW - consistency

KW - nonlinearity

KW - two-stage optimisation

UR - http://www.scopus.com/inward/record.url?scp=85121149842&partnerID=8YFLogxK

U2 - 10.1080/00207721.2021.2010833

DO - 10.1080/00207721.2021.2010833

M3 - 文章

AN - SCOPUS:85121149842

SN - 0020-7721

VL - 53

SP - 1503

EP - 1515

JO - International Journal of Systems Science

JF - International Journal of Systems Science

IS - 7

ER -

Semirecursive nonparametric algorithms for Hammerstein systems with stochastic autocorrelated input

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this