Comparison of some instrumental variable methods-Consistency and accuracy aspects
Automatica (Journal of IFAC)
Canonical structures in the identification of multivariable systems
Automatica (Journal of IFAC)
Identification of processes in closed loop-identifiability and accuracy aspects
Automatica (Journal of IFAC)
Brief paper: Maximum likelihood identification of noisy input-output models
Automatica (Journal of IFAC)
Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
An improved bias-compensation approach for errors-in-variables model identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Identification of continuous-time errors-in-variables models
Automatica (Journal of IFAC)
Identifiability of errors in variables dynamic systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: On the identifiability of errors-in-variables models with white measurement errors
Automatica (Journal of IFAC)
On-board Component Fault Detection and Isolation Using the Statistical Local Approach
Automatica (Journal of IFAC)
Brief paper: Instrumental variable method for systems with filtered white noise input
Automatica (Journal of IFAC)
Brief Optimal errors-in-variables filtering
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Strongly consistent coefficient estimate for errors-in-variables models
Automatica (Journal of IFAC)
Technical communique: Can errors-in-variables systems be identified from closed-loop experiments?
Automatica (Journal of IFAC)
Hi-index | 22.18 |
Most identification methods rely on the assumption that the input is known exactly. However, when collecting data under an identification experiment it may not be possible to avoid noise when measuring the input signal. In the paper some different ways to identify systems from noisy data are discussed. Sufficient conditions for identifiability are given. Also accuracy properties and the computational requirements are discussed. A promising approach is to treat the measured input and output signals as outputs of a multivariable stochastic system. If a prediction error method is applied using this approach the system will be identifiable under mild conditions.