A nonparametric kernel-based approach to Hammerstein system identification
type: Item
instance of: scholarly article
title: A nonparametric kernel-based approach to Hammerstein system identification
zbMATH Open document ID: 1375.93133
author: Riccardo Sven Risuleo
author: Giulio Bottegal
author: Håkan Hjalmarsson
published in: Automatica
publication date: 2017-11-22T00:00:00Z
full work available at URL: http://urn.kb.se/resolve
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93E20
zbMATH DE Number: 6810853
zbMATH Keywords: system identification
zbMATH Keywords: Hammerstein systems
zbMATH Keywords: nonlinear systems
zbMATH Keywords: kernel-based methods
zbMATH Keywords: Gaussian processes
describes a project that uses: PRMLT
MaRDI profile type: Publication
OpenAlex ID: W2756252287
cites work: An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
cites work: Identification of a modified Wiener-Hammerstein system and its application in electrically stimulated paralyzed skeletal muscle modeling
cites work: Convergence of the Iterative Hammerstein System Identification Algorithm
cites work: Q5483032
cites work: On the estimation of transfer functions, regularizations and Gaussian processes-revisited
cites work: Hammerstein-Wiener system estimator initialization
cites work: Kernels for Linear Time Invariant System Identification
cites work: Block-oriented nonlinear system identification
cites work: Subspace identification of Hammerstein systems using least squares support vector machines
cites work: Separable nonlinear least squares: the variable projection method and its applications
cites work: Non-parametric orthogonal series identification of Hammerstein systems
cites work: Continuous-time Hammerstein system identification
cites work: Identification of discrete Hammerstein systems using kernel regression estimates
cites work: Hammerstein system identification using nuclear norm minimization
cites work: The identification of nonlinear biological systems: Wiener and Hammerstein Cascade models
cites work: Iterative identification of Hammerstein systems
cites work: Q3996227
cites work: Maximum likelihood estimation via the ECM algorithm: A general framework
cites work: Generalized Kernel Regression Estimate for the Identification of Hammerstein Systems
cites work: Tuning complexity in regularized kernel-based regression and linear system identification: the robustness of the marginal likelihood estimator
cites work: Prediction error identification of linear systems: a nonparametric Gaussian regression approach
cites work: A new kernel-based approach for linear system identification
cites work: Kernel methods in system identification, machine learning and function estimation: a survey
cites work: A New Kernel-Based Approach for NonlinearSystem Identification
cites work: Frequency domain system identification with missing data
cites work: Q3394879
cites work: Parametric and nonparametric identification of linear systems in the presence of nonlinear distortions-a frequency domain approach
cites work: Identification of linear systems with nonlinear distortions
cites work: Nonlinear black-box modeling in system identification: A unified overview
cites work: Blind Maximum-Likelihood Identification of Wiener Systems
cites work: Identifying MIMO Hammerstein systems in the context of subspace model identification methods
cites work: Q3996207
cites work: Identification of Hammerstein-Wiener models
DOI: 10.1016/J.AUTOMATICA.2017.07.055
description: scientific article; zbMATH DE number 6810853