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Before choosing course

The course covers fundamentals of data based modeling, ranging from experiment design to estimation algorithms. The course is intended for PhD students in all areas that come in contact with data-based modeling. A common framework for most existing methods Is developed. The underlying theory as well as practical exercises are included.

Course offering missing for current semester as well as for previous and coming semesters
* Retrieved from Course syllabus FEL3201 (Spring 2019–)

Content and learning outcomes

Course contents

Signal spectra, linear time-invariant sytems, prediction and filtering, linear and non-linear models, identifiability, non-parametric methods, parameter estimation, maximum likelihood estimation, linear regression, least-squares estimation, the prediction error method, the instrumental variable method, subspace identification, kernel methods, support vector machines, convergence and consistency, modeling accuracy, Cramér-Rao lower bound, numerical optimization, recursive estimation, bias and variance errors, experiment design, applications oriented system identification, choice of identification criterion, model validation, model structure selection, system identification in practice.

Intended 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).

Course Disposition

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Literature and preparations

Specific prerequisites

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Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • EXA1 - Examination, 8,0 hp, betygsskala: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

·         15 min oral presentation of a selected topic in one of the lectures

·         80% on weekly home-work problems

·         project (preferably on a problem related to the student’s own research)

·         50 % on 72 h take home exam

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Profile picture Håkan Hjalmarsson

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FEL3201

Offered by

EECS/Decision and Control Systems

Main field of study

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Education cycle

Third cycle

Add-on studies

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Postgraduate course

Postgraduate courses at EECS/Decision and Control Systems