Lecture 2: Wednesday, May 10, 9.00-12.00
Model quality and model structure selection.
This lecture deals with the basic results on how to achieve good quality
in models, what model structures are useful and common and how to think
when to choose among different model structures.
Lecture 3: Wednesday, May 10, 15.00-17.00
Computer exercices.
Lecture 4: Thursday, May 11, 9.00-12.00
Experiment design and pre-processing of data.
In this lecture we will discuss how to make data maximally informative.
Of particular interest is how to deal with data collected in closed loop:
what are the possibilities and problems with that?
Lecture 5: Thursday, May 11, 15.00-17.00
System identification in practice.
In this lecture we will deal with how to approach the system identification
problem in practical terms. This lecture will also be open for questions
and various exposes over topics of interest to the audience.