Consistent identification of Wiener systems: a machine learning viewpoint
type: Item
instance of: scholarly article
title: Consistent identification of Wiener systems: a machine learning viewpoint
zbMATH Open document ID: 1364.93834
author: Gianluigi Pillonetto
published in: Automatica
publication date: 2017-06-02T00:00:00Z
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6725361
zbMATH Keywords: Bayesian estimation
zbMATH Keywords: Gaussian processes
zbMATH Keywords: kernel-based regularized methods
zbMATH Keywords: reproducing kernel Hilbert spaces
zbMATH Keywords: block oriented nonlinear structure
MaRDI profile type: Publication
full work available at URL: j.automatica.2013.06.005
OpenAlex ID: W1984009022
cites work: Q3993534
cites work: Theory of Reproducing Kernels
cites work: A blind approach to the Hammerstein-Wiener model identification
cites work: Nonparametric Bayesian regression
cites work: Estimating parameters and stochastic functions of one variable using nonlinear measurement models
cites work: On the estimation of transfer functions, regularizations and Gaussian processes-revisited
cites work: On the mathematical foundations of learning
cites work: Almost sure invariance principles for weakly dependent vector-valued random variables
cites work: Bayesian Analysis of Errors-in-Variables Regression Models
cites work: Consistent identification of NARX models via regularization networks
cites work: A new kernel-based approach for linear system identification
cites work: Sequential Monte Carlo Methods in Practice
cites work: Nonparametric identification of Wiener systems
cites work: Maximum likelihood identification of Wiener models
cites work: Strong consistence of recursive identification for Wiener systems
cites work: The identification of nonlinear biological systems: Wiener and Hammerstein Cascade models
cites work: A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
cites work: Prediction error identification of linear systems: a nonparametric Gaussian regression approach
cites work: A New Kernel-Based Approach for NonlinearSystem Identification
cites work: Subspace based approaches for Wiener system identification
cites work: Q3394879
cites work: Q3148817
cites work: Learning theory estimates via integral operators and their approximations
cites work: Q4413263
cites work: An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels
cites work: Learning from dependent observations
cites work: Blind Maximum-Likelihood Identification of Wiener Systems
cites work: Practical Approximate Solutions to Linear Operator Equations When the Data are Noisy
cites work: Q3996207
cites work: Optimal learning rates for least squares regularized regression with unbounded sampling
cites work: Identifying MIMO Wiener systems using subspace model identification methods
cites work: Q5732117
cites work: Conditions for linear processes to be strong-mixing
DOI: 10.1016/J.AUTOMATICA.2013.06.005
description: scientific article