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).
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