On the uniqueness of prediction error models for systems with noisy input-output data
Automatica (Journal of IFAC)
Identification and stochastic adaptive control
Identification and stochastic adaptive control
A Structure Theory for Linear Dynamic Errors-in-Variables Models
SIAM Journal on Control and Optimization
IEEE Transactions on Signal Processing
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)
Brief paper: Recursive identification for multivariate errors-in-variables systems
Automatica (Journal of IFAC)
Brief paper: On the identifiability of errors-in-variables models with white measurement errors
Automatica (Journal of IFAC)
Identification of errors-in-variables systems with nonlinear output observations
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
For the single-input-single-output (SISO) errors-in-variables system it is assumed that the system input is an ARMA process and that the driven noise of the system input and the observation noise are jointly Gaussian. The two-dimensional observation made on system input and output is represented as a two-dimensional (2D) ARMA system of minimum phase driven by a sequence of 2D i.i.d. Gaussian random vectors (innovation representation). It is shown that the resulting ARMA system is identifiable, i.e., its coefficients are uniquely defined under reasonable conditions. Recursive algorithms are proposed for estimating coefficients of the ARMA representation including those contained in the original SISO system. The estimates are proved to be convergent to the true values with probability one and the convergence rate is derived as well. The theoretical results are justified by numerical simulation.