System identification with generalized orthonormal basis functions
Automatica (Journal of IFAC) - Special issue on trends in system identification
Generalized Fourier and Toeplitz Results for Rational Orthonormal Bases
SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
Robust optimal experiment design for system identification
Automatica (Journal of IFAC)
Survey paper: Optimal experimental design and some related control problems
Automatica (Journal of IFAC)
On the equivalence of least costly and traditional experiment design for control
Automatica (Journal of IFAC)
A Matrix Handbook for Statisticians
A Matrix Handbook for Statisticians
Least costly identification experiment for control
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Hi-index | 22.14 |
In this paper we investigate the cost of complexity, which is defined as the minimum amount of input power required to estimate the frequency response of a given linear time invariant system of order n with a prescribed degree of accuracy. In particular we require that the asymptotic (in the data length) variance is less or equal to @c over a prespecified frequency range [0,@w"B]. The models considered here are Output Error models, with an emphasis on fixed denominator and Laguerre models. Several properties of the cost are derived. For instance, we present an expression which shows how the pole of the Laguerre model affects the cost. These results quantify how the cost of the system identification experiment depends on n and on the model structure. Also, they show the relation between the cost and the amount of information we would like to extract from the system (in terms of @w"B and @c). For simplicity we assume that there is no undermodelling.