Improved parameter estimates for non-linear dynamical models using a bootstrap method

DOI10.1080/00207170902854118zbMath1175.93220OpenAlexW2106333636MaRDI QIDQ3644823

Hua-Liang Wei, Stephen A. Billings

Publication date: 12 November 2009

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: http://eprints.whiterose.ac.uk/84819/1/acse%20research%20report%20888.pdf

zbMATH Keywords

bootstrapparameter estimationmeasurement uncertaintyerror-in-variablesNARX and NARMAX modelsnon-linear systems identificationorthogonal least-squares

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Related Items (2)

A robust model structure selection method for small sample size and multiple datasets problems ⋮ Nonlinear systems identification by combining regression with bootstrap resampling

Cites Work

• An Analysis of the Total Least Squares Problem
• Bootstrap methods: another look at the jackknife
• Computers and the Theory of Statistics: Thinking the Unthinkable
• Least squares parameter estimation algorithms for non-linear systems
• Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
• A prediction-error and stepwise-regression estimation algorithm for non-linear systems
• Orthogonal parameter estimation algorithm for non-linear stochastic systems
• The constrained total least squares technique and its applications to harmonic superresolution
• The Effect of Errors in the Independent Variables in Linear Regression
• The Acceptability of Regression Solutions: Another Look at Computational Accuracy
• Orthogonal least squares methods and their application to non-linear system identification
• Nonlinear model validation using correlation tests
• A bootstrap method for structure detection of NARMAX models
• Term and variable selection for non-linear system identification
• Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks

This page was built for publication: Improved parameter estimates for non-linear dynamical models using a bootstrap method