System identification: theory for the user
System identification: theory for the user
Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Generalized Fourier and Toeplitz Results for Rational Orthonormal Bases
SIAM Journal on Control and Optimization
Multivariable Feedback Control: Analysis and Design
Multivariable Feedback Control: Analysis and Design
Least costly identification experiment for control
Automatica (Journal of IFAC)
Closed-loop identification revisited
Automatica (Journal of IFAC)
Technical Communique: Asymptotic variance expressions for closed-loop identification
Automatica (Journal of IFAC)
Least-squares estimation of a class of frequency functions: A finite sample variance expression
Automatica (Journal of IFAC)
On the frequency domain accuracy of closed-loop estimates
Automatica (Journal of IFAC)
Analysis of the variability of joint input-output estimation methods
Automatica (Journal of IFAC)
Brief paper: Virtual Reference Feedback Tuning for non-minimum phase plants
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This paper deals with quantification of noise induced errors in identified discrete-time models of causal linear time-invariant systems, where the model error is described by the asymptotic (in data length) variance of the estimated poles and zeros. The main conclusion is that there is a fundamental difference in the accuracy of the estimates depending on whether the zeros and poles lie inside or outside the unit circle. As the model order goes to infinity, the asymptotic variance approaches a finite limit for estimates of zeros and poles having magnitude larger than one, but for zeros and poles strictly inside the unit circle the asymptotic variance grows exponentially with the model order. We analyze how the variance of poles and zeros is affected by model order, model structure and input excitation. We treat general black-box model structures including ARMAX and Box-Jenkins models.