Syllabus for System Identification


A revised version of the syllabus is available.


  • 5 credits
  • Course code: 1RT885
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Technology A1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2014-03-11
  • Established by:
  • Revised: 2018-08-30
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Spring 2019
  • 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.

  • Responsible department: Department of Information Technology

Learning outcomes

The aim of this course is to guide the student how to translate theoretical concepts into engineering practice.

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

  • describe the different phases that constitute the process of building models, from design identification experiment to model validation
  • account for and apply the stochastic concepts used in analysis of system identification methods, and to explain why different system identification methods and model structures are necessary in engineering practice
  • describe and motivate basic properties of identification methods like the least squares method and the prediction error method, and describe the principles behind recursive identification and its fields of application
  • find and apply techniques for identification of multivariable systems as state space representations
  • show working knowledge of the available tools, and to reason how to choose identification methods and model structures for real-life problems.
  • discuss relations to similar fields of research


The system identification problem - from data to model, recursive and batch. Model structures and input signals. The least squares and prediction error approaches. The stochastic setting. Model validation and practical aspects. Recursive identification schemes and applications (e.g. adaptive control). State space representations. Identification of multivariable (MIMO) systems. Orientation on realated research areas (e.g. machine learning).

The theoretical study will be complemented with a mini project based on realistic case studies.


Lectures, laboratory work and a mini project. Guest lecture.


Written examination (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.

Syllabus Revisions

Reading list

Reading list

Applies from: Spring 2019

Some titles may be available electronically through the University library.

Course literature

  • Pelckmans, Kristiaan Kompendium: Lecture Notes for a Course on System Identification

    Institutionen för informationsteknologi,

Optional reading

  • Söderström, Torsten; Stoica, Petre Gheorghe System identification

    Englewood Cliffs: Prentice-Hall, 1989

    Find in the library

  • Ljung, Lennart System identification : theory for the user

    2. ed.: Upper Saddle River, N.J.: Prentice Hall, cop. 1999

    Find in the library