van Overschee – Subspace Identification for Linear Systems

Argomenti del programma: The program of the course is as follows. Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX) Linear optimal prediction Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation) Black-box identification from measured data-sets of state-space models Variable estimation: Kalman Filter and…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The program of the course is as follows. Basic concepts of stochastic processes ARMA and ARMAX classes of parametric models for time series and for Input/Output systems Parameter identification of ARMA and ARMAX models Analysis of identification methods Model validation and pre-processing Time series analysis

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The program of the course is as follows. Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX) Linear optimal prediction Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation) Black-box identification from measured data-sets of state-space models Variable estimation: Kalman Filter and…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The program of the course is as follows. Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX) Linear optimal prediction Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation) Black-box identification from measured data-sets of state-space models Variable estimation: Kalman Filter and…