Identification of continuous systems
Identification of continuous systems
Numerical integration approach to on-line identification of continuous-time systems
Automatica (Journal of IFAC) - Identification and system parameter estimation
Sampling in digital signal processing and control
Sampling in digital signal processing and control
Computer-controlled systems (3rd ed.)
Computer-controlled systems (3rd ed.)
Identification of Continuous-Time Systems: Methodology and Computer Implementation
Identification of Continuous-Time Systems: Methodology and Computer Implementation
Digital Control and Estimation: A Unified Approach
Digital Control and Estimation: A Unified Approach
Identification of continuous-time models
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 |
Identification of continuous-time autoregressive processes from discrete-time data by replacing the differentiation operator by an approximation is considered. A linear regression model can then be formulated. The least-squares method and the instrumental variables method must be used with some care to get parameter estimates of good quality. The bias is studied explicitly in the paper together with the asymptotic distribution, and expressions are presented for the covariance matrix of the estimated parameters. It turns out that there are small differences in the dominating bias term for the different methods, whereas the statistical properties are comparable. Overall, the performance is similar to that of a prediction error method for short sampling intervals.