13E053MIP - System Modeling and Identification

Course specification
Course title System Modeling and Identification
Acronym 13E053MIP
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 6.0 Status elective
      Condition none
      The goal The objective of the course is to introduce students to different approaches to modeling and identification of continuous and discrete systems, both in terms of theoretical basis of system identification in time, frequency and complex domain, as well as in terms of practical aspects of identification through the use of computers in simulation, modeling and system identification .
      The outcome Students will be competent to plan and conduct the experiments and data acquisition that will enable quality modeling and identification of different systems, select appropriate model representation, and accordingly apply nonparametric and parametric system identification techniques, as well as perform validation and simulation the behavior of the obtained models.
      Contents of lectures System modeling. Nonparametric and parametric representation in time domain. Nonparametric identification: correlation analysis in time and frequency domain, empirical transfer function evaluation. Parametric identification in time domain: prediction error methods, linear and pseudo-linear least squares method, recursive identification, instrumental variables. Model validation.
      Contents of exercises Through different examples and assignments, students use the appropriate software to form and analyze the characteristic classes of linear and nonlinear models, solve identification problems of specific systems by various methods presented during theoretical lectures, examine the characteristics of different estimators, the notion of persistent excitation and the validity of the obtained model.
      1. Process identification, Kovačević B, Kvaščev G, Akademska misao, Beograd, 2018.
      2. System Identification: Theory for the User, Ljung L, Prentice-Hall, Englewood Cliffs, New Jersey, 1987.
      3. System identification toolbox: User's guide, Lennart Ljung, Natick, MA: MathWorks Incorporated, 1995.
      4. Modeling, Identification and Simulation of Dynamical Systems, P.P.J. van den Bosch, A.C. van der Klauw, CRC Press, 1994.
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      3 1 1
      Methods of teaching Lectures (45), exercises (15), computer exercises (15).
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures 0 Test paper 40
      Practical lessons 20 Oral examination 0
      Projects 0
      Colloquia 40
      Seminars 0