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Syllabus for

Academic year
SSY230 - System identification  
Syllabus adopted 2018-02-13 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering, Electrical Engineering

Teaching language: English
Open for exchange students: No
Block schedule: D

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0108 Examination 4,5c Grading: TH   4,5c   04 Jun 2019 pm M   12 Oct 2018 pm SB   Contact examiner
0208 Laboratory 3,0c Grading: UG   3,0c    

In programs



Jonas Sjöberg


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

Basic knowledge in automatic control, statistics, signals and systems.


The course aims to give the fundamental theory of identification of dynamical systems, i.e. how to use measured input-output data to build mathematical models, typically in terms of differential or difference equations. Further, the course aims to give fundamental knowledge of statistical learning, machine learning algorithms estimating relations in data, with the focus on dynamical systems.

Learning outcomes (after completion of the course the student should be able to)

  • understand and explain fundamental aspects on statistical learning and relate these to the estimation of dynamical
  • understand and explain the properties of the input signals for an identification experiment influence the quality of the estimated model.
  • understand and explain the possibilities and limitations concerning the quality of estimated models and on which factors these limitations depend.
  • understand and explain properties of different model structures and identification methods.
  • understand and use methods for validating estimated models.
  • understand and use computer tools for system identification.


The course includes:
  • Fundamentals on statistical learning where functions are estimated from data
  • The mathematical foundations of System Identification
  • Choice of model structure Linear and nonlinear models
  • Non-parametric techniques
  • Parametrizations and model structures
  • Parameter estimation
  • Asymptotic statistical theory
  • User choices
  • Experimental design
  • Recursive identification and adaptive control


The course comprises lectures and a number of hands on assignments/laboratory experiments that address important parts of the course.


Not decided yet

Examination including compulsory elements

Examination is based on written exam, grading scale TH, and passed assignment/laboration.

Published: Mon 28 Nov 2016.