Learning outcomes

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).