On the uniqueness of prediction error models for systems with noisy input-output data
Automatica (Journal of IFAC)
The Frisch scheme in dynamic system identification
Automatica (Journal of IFAC) - Identification and system parameter estimation
Identification of dynamic errors-in-variables models
Automatica (Journal of IFAC)
The set of observationally equivalent errors-in-variables models
Systems & Control Letters
A Structure Theory for Linear Dynamic Errors-in-Variables Models
SIAM Journal on Control and Optimization
Perspectives on errors-in-variables estimation for dynamic systems
Signal Processing
Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
Identifiability of errors in variables dynamic systems
Automatica (Journal of IFAC)
A covariance matching approach for identifying errors-in-variables systems
Automatica (Journal of IFAC)
Paper: Identification of scalar errors-in-variables models with dynamics
Automatica (Journal of IFAC)
Papers: Identification of stochastic linear systems in presence of input noise
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 | 22.15 |
We discuss identifiability of dynamic SISO errors-in-variables (EIV) models with white measurement errors. Although this class of models turns out to be generically identifiable, it has been pointed out that in certain circumstances there may be two EIV models which are indistinguishable from external input-output experiments. This lack of (global) identifiability may be prejudicial to identification and needs better understanding. The identifiability conditions found in the literature guarantee uniqueness under certain coprimality assumptions on the (rational) transfer function of the ideal ''true'' system and the spectral density of the noiseless ''true'' input. Unfortunately these conditions are not testable since they concern precisely the unknowns of the problem which are not available to the experimenter. We provide new identifiability conditions which are instead expressible in terms of the external description of the observable signals, namely their joint power spectral densities.