IAS0031 Modeling and Identification

Course description

  • Final results are in | 04.06.2019 20:22 | by Aleksei Tepljakov

    The results are published after the final exam attempt.

  • Results updated | 15.05.2019 10:51 | by Aleksei Tepljakov

    The results have been updated taking also the bonuses into account.

    Note that if you have not done any additional work but have received a bonus, it's due to a repeated attempt in submitting one of your lab reports.

  • Results published | 15.05.2019 09:26 | by Aleksei Tepljakov

    Dear Students,

    The results after the first round of course project presentations have been published. Some bonuses are not in yet, but each bonus is up to 2.5 points, so you can already estimate how many points you are missing towards your desired goal.

  • Exam times | 07.05.2019 16:57 | by Aleksei Tepljakov

    Dear Students,

    The exam times for the finals are as follows:

    1. May 28, 2019, 10.00-14.00.
    2. June 4, 2019, 10.00-14.00.

    These dates can be used to proceed with the following activities:

    • Present your course project (if unable to do so inside of the semester).
    • Redo one of the tests. Note that you cannot redo two tests in a single date. Best score choice policy is in effect (that is, if you do worse in your second attempt, your previous score is kept).

    If you do not get a mark inside of the semester, you will need to register to the exam via the OIS system.

    NB! Preliminary marks will appear in the Results section on this webpage after May 13, 2019. All bonus points that you may have amassed during the semester will be included in that table.






  • Spring



Awarded ECTS points: 


Link to e-learning environment: 



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 course include:

  • Optimization. Linear programming. Convexity. Least squares. Newton's Method. Simplex method. Nelder-Mead method (applications).
  • Static and dynamic models and applications.
  • Linear models. Time domain and frequency domain analysis.
  • Process models. Mathematical models of industrial processes.
  • Identification. Model types. Validation. Residual analysis.
  • Introduction to nonlinear systems. Kalman filter.
  • Global optimization methods. Genetic programming and symbolic regression.
  • Computational intelligence. Artificial Neural Networks. Modeling dynamic systems. Pattern recognition.
  • Deep learning. Image recognition with MATLAB and Python.

These topics will be delivered by several instructors 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 25% of the grade.
  • Five practice reports, each giving 5% of the grade.
  • An individual project report and presentation thereof giving 25% of the grade.

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

Test solutions, lab reports and the project are all sumbitted through TalTech Moodle.

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 for one of the test. Among the attempts, the one with the best grade will be counted as final.
  • 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 10-15 pages.

Regarding the project, the student must also

  • Submit the prospective project topic and abstract within the first 4 weeks of the semester;
  • Give a 10-12 minute talk about the finished project at the end of the course.

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 2020

Mon Tue Wed Thu Fri Sat Sun


Titlesort descending Published Short description Files
L01: Introduction 28.01.2019 Introduction to the subject and course L01_Introduction.pdf
L02.1: Linear programming. Optimization 23.05.2019 Linear programming. Optimization. Convexity. Practical optimization methods. L02-LP-Opt.pdf
L02.2: Simplex method 12.02.2019 Simplex Method. Nelder-Mead Method. Problems with bounds and constraints. L02-Simplex.pdf
L03: Modeling. Linear systems. 12.02.2019 Modeling. Linear systems. Frequency domain analysis. L03-Modeling.pdf
L04: Modeling. Differential equations representation. 25.02.2019 L04: Modeling. Differential equations representation. Steady-state. Linearization at operating point. Flow processes description. MaI19_L4s.pdf
L05: Energy Balance 05.03.2019 L05: Modeling. Thermal processes description. MaI19_L5s.pdf
L06: Heat transfer 05.03.2019 Heat transfer and Experimental estimations of the model parameters MaI19_L6s.pdf
L07: Identification 11.03.2019 Identification by linear models. L07_Identification.pdf
L08: Introduction to Nonlinear Systems 25.03.2019 Brief introduction to nonlinear systems and phase plane analysis. L08_Nonlinear_systems.pdf
L09: Global optimization. Symbolic regession 01.04.2019 Global optimization. Symbolic regression. L09_GlobalOpt.pdf
L10: Introduction to Artificial Neural Networks 15.04.2019 Artificial Neural Networks IAS0031_NN1_2019.pdf
L11: Identification using NNs 15.04.2019 Identification of Dynamic Systems using Artificial Neural Networks IAS0031_NN2_2019.pdf
L12: Pattern recognition using NNs 22.04.2019 Pattern recognition using NNs: Supervised Aapproach vs. Self Learning IAS0031_NN3_2019.pdf
L13: Deep Learning 29.04.2019 Deep learning class slides conv_class.pdf


Titlesort descending Published Short description Files
X01: Linear programming problems 12.02.2019 Exercises for formulating LPPs and solving them using the Graphical Method. L02_Exercises_Formulating_LP_Problems.pdf L02_Exercises_Solving_LP_Problems_using_GM.pdf
X02: The Kalman filter exercise 25.03.2019 Exercises for applying the linear Kalman filter L08_Kalman_Filter_Exercise.pdf lkf_files_MATLAB.zip