NDLI: On Nonparametric Identification of Wiener Systems

Content Provider	IEEE Xplore Digital Library
Author	Pawlak, M. Hasiewicz, Z. Wachel, P.
Copyright Year	1991
Abstract	In this paper, a new method for the identification of the Wiener nonlinear system is proposed. The system, being a cascade connection of a linear dynamic subsystem and a nonlinear memoryless element, is identified by a two-step semiparametric approach. The impulse response function of the linear part is identified via the nonlinear least-squares approach with the system nonlinearity estimated by a pilot nonparametric kernel regression estimate. The obtained estimate of the linear part is then used to form a nonparametric kernel estimate of the nonlinear element of the Wiener system. The proposed method permits recovery of a wide class of nonlinearities which need not be invertible. As a result, the proposed algorithm is computationally very efficient since it does not require a numerical procedure to calculate the inverse of the estimate. Furthermore, our approach allows non-Gaussian input signals and the presence of additive measurement noise. However, only linear systems with a finite memory are admissible. The conditions for the convergence of the proposed estimates are given. Computer simulations are included to verify the basic theory
Sponsorship	IEEE Signal Processing Society
Starting Page	482
Ending Page	492
Page Count	11
File Size	642672
File Format	PDF
ISSN	1053587X
Volume Number	55
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Kernel Nonlinear dynamical systems Convergence System identification Nonlinear systems Additive noise Noise measurement Parametric statistics Linear systems Wiener system least squares noninvertible nonlinearities nonlinear system identification nonparametric kernel estimate
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Identification of input-output bilinear systems using cumulants

Blind Maximum-Likelihood Identification of Wiener Systems

Continuous-time Hammerstein system identification from sampled data

Combined parametric-nonparametric identification of Hammerstein systems

On the use of system identification for accurate parametric modeling of nonlinear systems using noisy measurements

A Censored Sample Mean Approach to Nonparametric Identification of Nonlinearities in Wiener Systems

Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance

Identification of certain time-varying nonlinear Wiener and Hammerstein systems

Large sample properties of separable nonlinear least squares estimators

On Nonparametric Identification of Wiener Systems

Similar Documents

Identification of input-output bilinear systems using cumulants

Blind Maximum-Likelihood Identification of Wiener Systems

Continuous-time Hammerstein system identification from sampled data

Combined parametric-nonparametric identification of Hammerstein systems

On the use of system identification for accurate parametric modeling of nonlinear systems using noisy measurements

A Censored Sample Mean Approach to Nonparametric Identification of Nonlinearities in Wiener Systems

Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance

Identification of certain time-varying nonlinear Wiener and Hammerstein systems

Large sample properties of separable nonlinear least squares estimators

On Nonparametric Identification of Wiener Systems