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Syllabus for

Academic year
SSY230 - Learning dynamical systems using system identification
Maskininlärning av dynamiska system med systemidentifiering
 
Syllabus adopted 2019-02-14 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering, Electrical Engineering
Department: 32 - ELECTRICAL ENGINEERING


Teaching language: English
Application code: 35119
Open for exchange students: No
Block schedule: D

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0108 Examination 4,5c Grading: TH   4,5c   09 Oct 2020 pm J
0208 Laboratory 3,0c Grading: UG   3,0c    

In programs

MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)

Examiner:

Jonas Sjöberg


Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

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

Aim

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.

Content

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

Organisation

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

Literature

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.