Variance-error quantification for identified poles and zeros
Automatica (Journal of IFAC)
Adaptive Fourier series--a variation of greedy algorithm
Advances in Computational Mathematics
A regularised estimator for long-range dependent processes
Automatica (Journal of IFAC)
Rational Basis Functions for Robust Identification from Frequency and Time-Domain Measurements
Automatica (Journal of IFAC)
Quantifying the accuracy of Hammerstein model estimation
Automatica (Journal of IFAC)
The cost of complexity in system identification: The Output Error case
Automatica (Journal of IFAC)
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This paper provides a generalization of certain classical Fourier convergence and asymptotic Toeplitz matrix properties to the case where the underlying orthonormal basis is not the conventional trigonometric one but rather a rational generalization which encompasses the trigonometric one as a special case. These generalized Fourier and Toeplitz results have particular application in dynamic system estimation theory. Specifically, the results allow a unified treatment of the accuracy of least-squares system estimation using a range of model structures, including those that allow the inclusion of prior knowledge of system dynamics via the specification of fixed pole or zero locations.