Identification and Estimation Theory

Models and model representations. Parameter estimation, nonparametric methods: transient, frequency, correlation, and spectral analysis; parametric methods: Least squares (LS) methods, instrumental variables (IV) methods, prediction error (PE) methods; convergence and consistency analysis. Recursive methods and variants, stability and convergence of recursive methods. Identification in closed loop. Subspace methods for purely deterministic, purely stochastic, and mixed deterministic/stochastic linear systems.

The intention of the course is to give extensive knowledge in system identification.

Lectures and homework assignments.