ISS0031 Modeling and Identification

Course description

Syllabus

Instructors: 

Semester: 

  • Autumn

Year: 

2017

Awarded ECTS points: 

5.0

Attachment: 

Description: 

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.

Policies: 

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.

Calendar

Important events

Datesort ascending Title Type Location
10.10.2017 - 18:30 to 21:00 T01: Test #1 Test U02-303/304
10.10.2017 - 17:45 to 18:30 K02: Second project talk Event U02-309

September 2017

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Materials

Lectures

Title Published Short description Files
L03: Optimization. Convexity. Practical optimization. 19.09.2017 Optimization. Convexity. Newton's Method. Least squares. L03-Optimization_Convexity.pdf L03_Exercises.pdf
L02: Modeling. Linear programming. 12.09.2017 Modeling. Model types. Applications. Linear programming. Graphical method. L02-Modeling_Linear_Programming-share.pdf
L01: Introduction 05.09.2017 Introduction. Course content and policies. Project topics. L01-Introduction.pdf

Exercises

Title Published 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

Laboratory works

Title Published 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
Assignments
Title Published 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