Course syllabus for Learning dynamical systems using system identification

The course syllabus contains changes
See changes

Course syllabus adopted 2021-02-26 by Head of Programme (or corresponding).

Overview

  • Swedish nameMaskininlärning av dynamiska system med systemidentifiering
  • CodeSSY230
  • Credits7.5 Credits
  • OwnerMPSYS
  • Education cycleSecond-cycle
  • Main field of studyAutomation and Mechatronics Engineering, Electrical Engineering
  • DepartmentELECTRICAL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 35113
  • Block schedule
  • Open for exchange studentsNo

Credit distribution

0108 Examination 4.5 c
Grading: TH		 4.5 c
  • 31 Maj 2022 pm J
  • 08 Okt 2021 pm J
  • Contact examiner
0208 Laboratory 3 c
Grading: UG		 3 c

In programmes

Examiner

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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.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

The course syllabus contains changes

  • Changes to examination:
    • 2021-09-21: Grade raising Changed to grade raising by GRULG