Identification of ARX and ARARX models in the presence of input and output noises

DOI10.3166/EJC.16.242-255zbMath1198.93068MaRDI QIDQ2638170

Roberto Diversi, Umberto Soverini, Roberto P. Guidorzi

Publication date: 14 September 2010

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

zbMATH Keywords

system identificationARX modelserrors-in-variables modelsARARX modelsdynamic Frisch scheme

Mathematics Subject Classification ID

System identification (93B30) Identification in stochastic control theory (93E12)

Related Items (6)

Identification of errors-in-variables ARX models using modified dynamic iterative PCA ⋮ Identification of EIV models by compensated PEM ⋮ Optimal experiment design for identification of ARX models with constrained output in non-Gaussian noise ⋮ Design of normalized fractional adaptive algorithms for parameter estimation of control autoregressive autoregressive systems ⋮ A unified framework for EIV identification methods when the measurement noises are mutually correlated ⋮ Bias-eliminating least-squares identification of errors-in-variables models with mutually correlated noises

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Maximum likelihood estimation of the dynamic shock-error model
• The Frisch scheme in dynamic system identification
• An indirect prediction error method for system identification
• Maximum likelihood identification of noisy input-output models
• Identification of stochastic linear systems in presence of input noise
• Common factor detection and estimation
• Perspectives on errors-in-variables estimation for dynamic systems
• Identifiability of errors in variables dynamic systems
• Variance properties of a two-step ARX estimation procedure
• Identification of linear systems with input and output noise: the Koopmans-Levin method
• EFFICIENT ESTIMATION OF PARAMETERS IN MOVING-AVERAGE MODELS
• Comparison of total least squares and instrumental variable methods for parameter estimation of transfer function models
• On-line estimation of dynamic shock-error models based on the Kullback Leibler information measure
• Identification of autoregressive models in the presence of additive noise
• Errors-in-variables methods in system identification

This page was built for publication: Identification of ARX and ARARX models in the presence of input and output noises