Entry requirements

120 credits including Linear Algebra II, Probability and Statistics, Signals and Systems, and Signal Processing or Automatic

Control II. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• describe the different phases that constitute the process of building models, from design of the identification experiment to model validation
• analyze system identification methods using statistical methods
• describe and motivate basic properties of classical identification methods
• show working knowledge of the available tools and software
• discuss relations to similar fields of research

Content

Examples of models for systems and signals. System identification methods, including linear regression, maximum likelihood, the prediction error method and subspace methods. Statistical properties of different methods. Black box and gray box modeling. Model validation and practical aspects. Orientation on non-linear modeling and related research areas (eg machine learning).

Instruction

Lectures, problem solving sessions, mini project and laboratory work.

Assessment

Written exam (3 credits), laboratory work (1 credit) and mini project (1 credit).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other regulations

Cannot be included in the same degree as 1RT875, 1RT880 or 1RT890.