System identification: theory for the user
System identification: theory for the user
System identification with generalized orthonormal basis functions
Automatica (Journal of IFAC) - Special issue on trends in system identification
Orthonormal Basis Functions in Time and Frequency Domain: Hambo Transform Theory
SIAM Journal on Control and Optimization
Non-asymptotic confidence regions for model parameters in the presence of unmodelled dynamics
Automatica (Journal of IFAC)
Brief Non-asymptotic confidence ellipsoids for the least-squares estimate
Automatica (Journal of IFAC)
Guaranteed non-asymptotic confidence regions in system identification
Automatica (Journal of IFAC)
Least-squares estimation of a class of frequency functions: A finite sample variance expression
Automatica (Journal of IFAC)
Non-asymptotic quality assessment of generalised FIR models with periodic inputs
Automatica (Journal of IFAC)
Non-asymptotic confidence regions for model parameters in the presence of unmodelled dynamics
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This paper deals with the problem of constructing confidence regions for the parameters of truncated series expansion models. The models are represented using orthonormal basis functions, and we extend the 'Leave-out Sign-dominant Correlation Regions' (LSCR) algorithm such that non-asymptotic confidence regions for the parameters can be constructed in the presence of unmodelled dynamics. The constructed regions have guaranteed probability of containing the true parameters for any finite number of data points. The algorithm is first developed for FIR models and then extended to models with generalized orthonormal basis functions. The usefulness of the developed approach is demonstrated for FIR and Laguerre models in simulation examples.