State-space LPV model identification using kernelized machine learning
type: Item
instance of: scholarly article
title: State-space LPV model identification using kernelized machine learning
zbMATH Open document ID: 1379.93094
author: Syed Zeeshan Rizvi
author: Javad Mohammadpour Velni
author: Farshid Abbasi
author: Roland Tóth
author: Nader Meskin
published in: Automatica
publication date: 2018-01-31T00:00:00Z
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93E35
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 93E24
zbMATH DE Number: 6832885
zbMATH Keywords: nonparametric identification
zbMATH Keywords: linear parameter-varying models
zbMATH Keywords: support vector machines
zbMATH Keywords: kernels
MaRDI profile type: Publication
full work available at URL: j.automatica.2017.11.004
OpenAlex ID: W2780439059
cites work: 10.1162/153244303768966085
cites work: Identification of linear parameter varying models
cites work: On the mathematical foundations of learning
cites work: Subspace identification of MIMO LPV systems using a periodic scheduling sequence
cites work: Convergence analysis of kernel canonical correlation analysis: theory and practice
cites work: Choice of state-space basis in combined deterministic-stochastic subspace identification
cites work: Q3394879
cites work: Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
cites work: Modeling and identification of linear parameter-varying systems
cites work: Subspace identification of bilinear and LPV systems for open- and closed-loop data
cites work: Subspace identification of multivariable linear parameter-varying systems
cites work: Kernel methods for subspace identification of multivariable LPV and bilinear systems
DOI: 10.1016/J.AUTOMATICA.2017.11.004
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description: scientific article; zbMATH DE number 6832885