Identification of polynomial input/output recursive models with simulation error minimisation methods

DOI10.1080/00207721.2010.496055zbMath1259.93121OpenAlexW2073187541MaRDI QIDQ4909280

Marcello Farina, Luigi Piroddi

Publication date: 12 March 2013

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2010.496055

zbMATH Keywords

nonlinear modelsmodel identificationprediction error minimization methodsNARX modelssimulation error minimization methods

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Perturbations in control/observation systems (93C73) Identification in stochastic control theory (93E12)

Related Items (4)

A randomized algorithm for nonlinear model structure selection ⋮ Simulation error minimization identification based on multi-stage prediction ⋮ Discrete-time filtering for nonlinear polynomial systems over linear observations ⋮ On the smoothness of nonlinear system identification

Cites Work

• Dynamical effects of overparametrization in nonlinear models
• Nonlinear black-box modeling in system identification: A unified overview
• Structure identification of nonlinear dynamic systems - A survey on input/output approaches
• Model selection approaches for non-linear system identification: a review
• Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
• Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
• Orthogonal parameter estimation algorithm for non-linear stochastic systems
• Application of a general multi-model approach for identification of highly nonlinear processes-a case study
• Recursive algorithm for computing the frequency response of a class of non-linear difference equation models
• EFFECTS OF THE SAMPLING TIME ON THE DYNAMICS AND IDENTIFICATION OF NONLINEAR MODELS
• An identification algorithm for polynomial NARX models based on simulation error minimization
• Improved structure selection for nonlinear models based on term clustering
• General model-set design methods for multiple-model approach

This page was built for publication: Identification of polynomial input/output recursive models with simulation error minimisation methods