Continuous-time approaches to system indentification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
Numerical integration approach to on-line identification of continuous-time systems
Automatica (Journal of IFAC) - Identification and system parameter estimation
The effects of rapid sampling in system identification
Automatica (Journal of IFAC) - Identification and system parameter estimation
Delta Levinson and Schur-type RLS algorithms for adaptive signalprocessing
IEEE Transactions on Signal Processing
Continuous-time identification of SISO systems using Laguerre functions
IEEE Transactions on Signal Processing
Identification of continuous-time models
IEEE Transactions on Signal Processing
Stochastic theory of continuous-time state-space identification
IEEE Transactions on Signal Processing
Parameter estimation for continuous-time models-A survey
Automatica (Journal of IFAC)
Brief paper: On the indirect approaches for CARMA model identification
Automatica (Journal of IFAC)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling continuous-time processes via input-to-state filters
Automatica (Journal of IFAC)
Hi-index | 22.15 |
When identifying a continuous-time AR process from discrete-time data, an obvious approach is to replace the derivative operator in the continuous-time model by an approximation. In some cases, a linear regression model can then be formulated. The well-known least-squares method would be very desirable to apply, since it enjoy good numerical properties and low computational complexity, in particular for fast or nonuniform sampling. The focus of this paper is the latter, i.e., nonuniform sampling. Two consistent least-squares schemes for the case of unevenly sampled data are presented. The precise choice of derivative approximation turns out to be crucial. The obtained results are compared to a prediction error method.