Syllabus for System Identification



  • 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: 2021-02-04
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2021
  • 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 of the identification experiment to model validation
  • analyze system identification methods using statistical methods
  • describe and motivate basic properties of classical identification methods
  • show working knowledge of the available tools and software
  • discuss relations to similar fields of research


Examples of models for systems and signals. Correlation analysis and spectral analysis. Linear regression, the least squares method and prediction error methods. Black box and gray box modeling. Model validation and practical aspects. Possibilities and limitations with empirical modeling. Recursive identification methods and applications. Orientation on non-linear modeling and related research areas (eg machine learning).


Lectures, problem solving sessions, mini project and laboratory work.


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.

Reading list

Reading list

Applies from: Autumn 2021

Some titles may be available electronically through the University library.

  • Ljung, Lennart; Glad, Torkel Modellbygge och simulering

    2., [utvidgade och modifierade] uppl.: Lund: Studentlitteratur, 2004

    Find in the library

  • Ljung, Lennart; Glad, Torkel; Hansson, Anders Modeling and identification of dynamic systems

    Second edition: Lund: Studentlitteratur, [2021]

    Find in the library