Errors-in-variables identification using maximum likelihood estimation in the frequency domain
From MaRDI portal
Publication:2409336
DOI10.1016/j.automatica.2017.01.016zbMath1371.93210OpenAlexW2591941256MaRDI QIDQ2409336
Umberto Soverini, Torsten Söderström
Publication date: 11 October 2017
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2017.01.016
system identificationdiscrete Fourier transformerrors-in-variables modelslinear dynamic systemsmaximum likelihood identification
Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)
Related Items (20)
A combined invariant-subspace and subspace identification method for continuous-time state-space models using slowly sampled multi-sine-wave data ⋮ Multi-step-length gradient iterative algorithm for equation-error type models ⋮ Identification of dynamic errors-in-variables systems with quasi-stationary input and colored noise ⋮ Weight least squares algorithm for rational models with outliers ⋮ Identification of errors-in-variables systems with general nonlinear output observations and with ARMA observation noises ⋮ Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle ⋮ Auxiliary variable-based identification algorithms for uncertain-input models ⋮ Recursive identification of errors-in-variables systems based on the correlation analysis ⋮ A generalized minimal residual based iterative back propagation algorithm for polynomial nonlinear models ⋮ A graph subspace approach to system identification based on errors-in-variables system models ⋮ A note on the estimation of real- and complex-valued parameters in frequency domain maximum likelihood identification ⋮ Global convergence of the EM algorithm for ARX models with uncertain communication channels ⋮ Frequency domain identification of FIR models in the presence of additive input-output noise ⋮ A recursive identification algorithm for Wiener nonlinear systems with linear state-space subsystem ⋮ Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition ⋮ Data filtering based maximum likelihood gradient estimation algorithms for a multivariate equation-error system with ARMA noise ⋮ Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory ⋮ Parameter estimation for a controlled autoregressive autoregressive moving average system based on a recursive framework ⋮ Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique ⋮ Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Accuracy analysis of a covariance matching approach for identifying errors-in-variables systems
- A generalized instrumental variable estimation method for errors-in-variables identification problems
- On covariance function tests used in system identification
- Maximum likelihood identification of noisy input-output models
- Frequency domain maximum likelihood estimation of linear dynamic errors-in-variables models
- On the equivalence of time and frequency domain maximum likelihood estimation
- A covariance matching approach for identifying errors-in-variables systems
- Identification of stochastic linear systems in presence of input noise
- Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets
- Perspectives on errors-in-variables estimation for dynamic systems
- Discrete-time stochastic systems. Estimation and control.
- Errors-in-variables identification of dynamic systems excited by arbitrary non-white input
- Identification of linear systems with input and output noise: the Koopmans-Levin method
- Frequency domain system identification using arbitrary signals
- Frequency domain system identification with missing data
- Errors-in-variables methods in system identification
This page was built for publication: Errors-in-variables identification using maximum likelihood estimation in the frequency domain