After the course, the student should be able to:
- describe the general principles for system identification.
- identify systems in a satisfactory manner. This includes choice of excitation signals, model structure and estimation algorithm as well as proper use of model validation.
- analyse basic model properties, such as identifiability and accuracy (bias and variance errors).