CONTENTS
Introduction. Stochastic background. Nonparametric methods in identification. Least--squares, static models, BLUE estimators.
Input signals, persistent excitation Least-squares, dynamic methods. Prediction error methods, dynamic models. Instrumental variable methods. Recursive identification methods. Closed loop systems identification. Model validation.

LABS
1. Least-squares method
2. Instrumental variabel method
3. Method's benchmark
4. Recursive identification

TEACHING
The teaching consists of lectures, and four compulsory laboratory excercises.

EXAMINATION

COURSE GRADE SCALE: U, 3, 4, 5

ITEMS/CREDITS