Causal regression for online estimation of highly nonlinear parametrically varying models

DOI10.1016/j.automatica.2020.109425zbMath1461.93492OpenAlexW3114272890MaRDI QIDQ2663922

Elvis Jara Alegria, Celso Pascoli Bottura, Mateus Giesbrecht

Publication date: 20 April 2021

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2020.109425

zbMATH Keywords

nonlinear regressiononline estimationhighly nonlinear modelcausal predictor

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10)

Related Items (1)

An associative-memory-based method for system nonlinearities recursive estimation

Uses Software

• CAPTAIN

Cites Work

• Unnamed Item
• Nonlinear system identification using quasi-ARX RBFN models with a parameter-classified scheme
• Identification of nonlinear system structure and parameters using regime decomposition
• True Digital Control
• Recursive Estimation and Time-Series Analysis
• Identification of state-dependent parameter models with support vector regression
• Constructing NARMAX models using ARMAX models
• State-dependent parameter models of non-linear sampled-data systems: a velocity-based linearization approach
• Piecewise linear identification of non-linear systems
• Identification of a class of non-linear parametrically varying models
• Structured parameter optimization method for the radial basis function-based state-dependent autoregressive model
• Identification of systems from non-uniformly sampled data
• Research on gain scheduling
• Stochastic Equations in Infinite Dimensions

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