System identification


Basics on probability theory.

Basics on estimation theory. Optimality criteria: centering, consistency, efficiency. Rao-Cramer lower-bound for covariance.

Estimate types: least square estimates, maximal likelihood estimates and Bayesian estimates.

Kalman filtering and prediction. Steady state Kalman filter and asympotitc optimality. Kalman filter for correlated input and measurement noise sequences.

Parametric identification. Identification with test inputs/control inputs. Identification for input-output and state space models. AR, MA and ARMA models.


Available Exams (pdf files)

The teacher receives the students each Friday from 15.00 until 18.00.

freccia su