System identification: theory for the user
System identification: theory for the user
Consistent identification of stochastic linear systems with noisy input-output data
Automatica (Journal of IFAC)
Time series: data analysis and theory
Time series: data analysis and theory
Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
Identification of continuous-time errors-in-variables models
Automatica (Journal of IFAC)
Identification of Continuous-time Models from Sampled Data
Identification of Continuous-time Models from Sampled Data
Noisy input/output system identification using cumulants and theSteiglitz-McBride algorithm
IEEE Transactions on Signal Processing
Parameter estimation for continuous-time models-A survey
Automatica (Journal of IFAC)
Closed-loop identification revisited
Automatica (Journal of IFAC)
Instrumental variable methods for closed-loop system identification
Automatica (Journal of IFAC)
An instrumental variable method for real-time identification of a noisy process
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Fast frequency template matching using higher order statistics
IEEE Transactions on Image Processing
Computers & Mathematics with Applications
| Hi-index | 22.15 |
In this paper, the problem of identifying stochastic linear continuous-time systems from noisy input/output data is addressed. The input of the system is assumed to have a skewed probability density function, whereas the noises contaminating the data are assumed to be symmetrically distributed. The third-order cumulants of the input/output data are then (asymptotically) insensitive to the noises, that can be coloured and/or mutually correlated. Using this noise-cancellation property two computationally simple estimators are proposed. The usefulness of the proposed algorithms is assessed through a numerical simulation.