Instrumental variables approach to identification of a class of MIMO Wiener systems
type: Item
instance of: scholarly article
title: Instrumental variables approach to identification of a class of MIMO Wiener systems
zbMATH Open document ID: 1177.93090
author: Andrzej Janczak
published in: Nonlinear Dynamics
publication date: 2009-09-22T00:00:00Z
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93E03
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93C05
zbMATH DE Number: 5605794
zbMATH Keywords: instrumental variables method
zbMATH Keywords: linear regression
zbMATH Keywords: multi-input multi-output nonlinear systems
zbMATH Keywords: nonlinear system identification
zbMATH Keywords: parameter estimation
zbMATH Keywords: Wiener models
MaRDI profile type: Publication
full work available at URL: s11071-006-9088-y
OpenAlex ID: W2053692880
cites work: Use of multilayer feedforward neural networks in identification and control of Wiener model
cites work: Identification of linear systems with hard input nonlinearities of known structure
cites work: Frequency domain identification of Wiener models
cites work: Identification of systems containing linear dynamic and static nonlinear elements
cites work: Nonparametric identification of Wiener systems by orthogonal series
cites work: Q4329684
cites work: Comparison of four gradient-learning algorithms for neural network Wiener models
cites work: Q4411302
cites work: Identification of nonlinear systems using neural networks and polynomial models. A block-oriented approach.
cites work: Identification of Wiener-type nonlinear systems in a noisy environment
cites work: Identification of time-varying pH processes using sinusoidal signals
cites work: Q4227814
cites work: Q4263623
cites work: Adaptive control of Wiener type nonlinear systems
cites work: Q3993512
cites work: Identifying MIMO Wiener systems using subspace model identification methods
cites work: Parameter identification of Wiener systems with discontinuous nonlinearities
cites work: Recusrsive prediction error identification using the nonlinear Wiener model
cites work: Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model
DOI: 10.1007/S11071-006-9088-Y
Recommended article: Identification for disturbed MIMO Wiener systems
Recommended article: Recursive Identification of MIMO Wiener Systems
Recommended article: MIMO system identification: State-space and subspace approximations versus transfer function and instrumental variables
Recommended article: Identifying MIMO Wiener systems using subspace model identification methods
Recommended article: Linear approximation and identification of MIMO Wiener-Hammerstein systems
Recommended article: Optimally conditioned instrumental variable approach for frequency-domain system identification
Recommended article: On Nonparametric Identification of Wiener Systems
Recommended article: Two-stage instrumental variables identification of polynomial Wiener systems with invertible nonlinearities
Recommended article: Nonparametric identification of Wiener systems
Recommended article: Blind Maximum-Likelihood Identification of Wiener Systems
description: scientific article; zbMATH DE number 5605794