ISS0031 Modeling and Identification

Course description

  • End of Semester Announcement | 15.12.2017 15:24 | by Aleksei Tepljakov

    Dear Students, there will be no further activities until finals in January. Enjoy the holidays!

  • Results Updated after Test #2 | 07.12.2017 18:55 | by Aleksei Tepljakov

    The results table has been updated after the evaluation of Test #2. See updated information here.

  • Test #2 | 28.11.2017 21:01 | by Aleksei Tepljakov
    The second test will take place on December 5, 2017. Details follow:
    • The test starts at 18.00 and the allocated time for it is three hours.
    • NB! This test will be an open-book test.
    • The maximum (nominal) number of course points awarded is 20.
    • The test will contain several questions related to theory: you will have to show your skills in applying the methods that you have learned about to modeling problems.
    There will be no practical work after the test.
  • Exam Dates during Finals | 28.11.2017 20:58 | by Aleksei Tepljakov

    The exam dates are as follows:

    • January 9, 2016, 10.00-13.00. Room U02-304.
    • January 16, 2016, 10.00-13.00. Room U02-304.

    During the exam times you can either redo a test for which you want to improve your mark, or you can do your project presentation.

    NB! Project report must be submitted at least one day before the date of the corresponding exam!

  • No Lecture this Tuesday | 06.11.2017 17:32 | by Aleksei Tepljakov

    Dear Students, unfortunately, the lecture/practical work for Nonlinear systems that was scheduled for this Tuesday, 07.11.2017, must be canceled due to the instructor taking an unexpected sick leave. Depending on how long the sick leave lasts, the schedule may be futher rearranged to include the remaining invited lectures. Since it is too late to find a substitute for this one, there will be no lecture/practical work tomorrow. Please follow the news regarding future events.

  • Order of Lectures | 30.10.2017 17:57 | by Aleksei Tepljakov

    Dear Students, as you have seen, the planned lectures have been shifted one week ahead of schedule. Here are some updates: This week, we'll have an invited lecture with Prof. Eduard Petlenkov on the topic of applying Artificial Neural Networks to modeling and identification problems. The practical work that follows the lecture requires a report to be submitted.

    Next week, we'll have the Nonlinear systems lecture and corresponding practical work. Note, that the latter does not require a report to be submitted. However, if you'd like, you can still prepare and submit a report for Nonlinear systems topic for bonus points. Please follow the calendar to learn the new order of the lectures.

  • Test #1 | 03.10.2017 19:12 | by Aleksei Tepljakov
    The first test will take place on October 17, 2017.
    Please note that it was shifted from October 10 due to some circumstances. Details follow:
    • The test starts at 18.00 and the allocated time for it is three hours.
    • NB! This test will be a closed-book test.
    • The maximum (nominal) number of course points awarded is 20.
    • The test will comprise several practical assignments related to linear programming:
      • Formulating a linear programming problem given a particular task,
      • Solving it using the graphical approach and the Simplex method.
    • The test will also contain several questions related to theory.
    Also, there will be no practical work after the test.
  • Simplex Method Tutor Available | 26.09.2017 19:43 | by Aleksei Tepljakov

    Dear Students, for your convenience, a basic Simplex Method tutor has been made available so that you can check your Simplex Method-based linear programming problem solutions:




  • Autumn



Awarded ECTS points: 




The aim of the course is to give an overview of modeling and identification methods for solving static and dynamic problems such as optimal resource planning and industrial control. The major topics covered in the first part of the course include:

  • Static and dynamic models and applications.
  • Optimization. Linear programming. Convexity. Least squares. Newton's Method. Simplex method. Nelder-Mead method (applications).
  • Linear models. Time domain and frequency domain analysis.
  • Identification. Model types. Validation. Residual analysis.

In the second part of the course, the following contemporary modeling topics will be discussed:

  • Fractional-order modeling and control.
  • Artificial Neural Network based identification.
  • Global optimization methods. Genetic algorithms.
  • Fuzzy logic based modeling.

These topics will be delivered during several invited lectures and will be accompanied by corresponding practical works.

Most of the practical assignments of the course will be solved in MATLAB/Simulink environment. The first practice will be given on the 3rd week of the semester.


The learning outcomes of the course are evaluated in the following way:

  • Two tests, each giving 20% of the grade.
  • Five practice reports, each giving 2% of the grade.
  • An individual project report and presentation thereof giving 50% of the grade.
  • Bonuses: reports for practical works where a report is not required by default; other bonuses also possible.

All of these components are summed up at the end of the semester and form the exam grade.

The following policies are in effect:

  • There is only one attempt to do each of the tests during the semester. It is however possible to improve the result (if desired) during finals. Among the attempts, the one with the best grade will be counted as final.
  • The practice reports will cover topics from the second part of the course. The report for each practice must be submitted within two weeks of the date of carrying out the practical work in the laboratory. If a report is not received within the allocated time interval, the grade points for the practical work are not awarded.
  • Topics for the individual project may be selected from a list offered by the instructor, or proposed by the student. In the latter case, the topic of the project must be within the scope of the course. At the end of the course, a report for the project must be prepared and submitted for evaluation. The prospective length of the report is 15-20 pages.

Regarding the project, the student must also

  • Give short, 3-5 minute talks:
    • The first, describing his project idea, must be presented on the 3th week of the semester;
    • The second, presented on the 6th week, must provide an update illustrating the progress;
  • Give a 10-12 minute talk about the finished project at the end of the course.

On all occasions, the instructor and other students may give feedback about the project.

The project may also be a work-in-progress. In such a case, the results obtained by the end of the course must clearly demonstrate the advances in the developed topic.


January 2018

Mon Tue Wed Thu Fri Sat Sun


Title Publishedsort descending Short description Files
L01: Introduction 05.09.2017 Introduction. Course content and policies. Project topics. L01-Introduction.pdf
L02: Modeling. Linear programming. 12.09.2017 Modeling. Model types. Applications. Linear programming. Graphical method. L02-Modeling_Linear_Programming-share.pdf
L03: Optimization. Convexity. Practical optimization. 19.09.2017 Optimization. Convexity. Newton's Method. Least squares. L03-Optimization_Convexity.pdf L03_Exercises.pdf
L04: Simplex Method 26.09.2017 Simplex Method. Nelder-Mead Direct Search Method. Problems with bounds and constraints. L04-Simplex_Method.pdf
L05: Linear systems 03.10.2017 Linear systems. Process models. Frequency domain analysis. L05_Linear_systems.pdf LaplaceTable.pdf
L06: Identification 10.10.2017 Identification by linear approximations. Validation and residual analysis. Discrete-time systems. Experiment design. L06_Identification.pdf
L07: Fractional-order Calculus 24.10.2017 Fractional-order Calculus in modeling and control. Theory and applications. CACSD tool overview. L07_FracCalc.pdf
L08: Artificial Neural Networks 30.10.2017 Artificial Neural Networks for modeling and control. NN_Ident2015.pdf
L09: Introduction to Nonlinear Systems 14.11.2017 Introduction to Nonlinear Systems. Second order systems. Phase plane analysis. L09_Nonlinear_systems.pdf
L10: Fuzzy Logic 21.11.2017 Fuzzy logic in Modeling and Identification. Sergei_Astapov_Fuzzy_Control_lecture_slides.pdf
L11: Global Optimization 28.11.2017 Global Optimization. Symbolic Regression. L11_GlobalOpt.pdf
L12: Nonlinear systems II. Modeling of Power Systems. 12.12.2017 Nonlinear systems II: Algebraic approach. Modeling of Power Systems. Belikov_NLS.pdf Belikov_slides_PS.pdf


Title Publishedsort descending Short description Files
L02: Exercises 18.09.2017 Exercises on formulating linear programming problems and on using the graphical method to solve problems with two decision variables L02_Exercises_Formulating_LP_Problems.pdf L02_Exercises_Solving_LP_Problems_using_GM.pdf
L04: Exercises 26.09.2017 Exercises on solving linear programming problems using the Simplex Method L04_Exercises_Simplex_Method.pdf

Laboratory works

Title Publishedsort descending Short description Files
P01: Introduction to MATLAB 19.09.2017 Introduction to MATLAB. Anonymous functions. Newton's method. Least squares. Lab_01.pdf pract.m
P02: Linear programming 26.09.2017 Linear programming. Nelder-Mead Direct Search method. Lab_02_exercises.pdf P02_2017_09_26.m
P03: Linear systems. Transfer functions 03.10.2017 Exercises and materials for linear systems and control design. Lab_03_CSToolbox_functions.pdf Lab_03_Exercises.pdf pr003_01.m pr003_02.m
P04: Introduction to nonlinear systems 14.11.2017 Analysis of second-order nonlinear systems using pplane8. Feedback linearization. Lab_04_Exercises.pdf feedback_linearization_model_pendulum.slx
Title Publishedsort descending Short description Files
PRJ0: Project topic presentation 11.09.2017 Slide template for presenting your project topic on the 3rd week of the semester (Powerpoint format). ISS0031_Project_presentation_slide_template.pptx
*P01: Identification by linear models 10.10.2017 Materials for the first reported laboratory work: Identification by linear models R_Lab_01.pdf lab4_datasets.mat identification_test.m vplot.m identification_test_session.sid
*P02: Fractional-order Modeling and Control 24.10.2017 Reported laboratory work: Identification by fractional-order model. Fractional-order PID controller design. R_Lab_02.pdf
*P03: Artificial Neural Networks 30.10.2017 Applying Artificial Neural Networks to modeling and identification tasks R_Lab_03.pdf
*P04: Fuzzy Logic 21.11.2017 Assignments related to Fuzzy Logic based modeling fuzzy_lab_instructions.pdf
*P05: Global Optimization 29.11.2017 Global Optimization. Symbolic Regression. R_Lab_05.pdf mobj_optim_godlike.m