ISS0031 Modeling and Identification

Course description

News
  • Results page updated | 17.10.2018 14:45 | by Aleksei Tepljakov

    Dear students, you can now view the results of the first test in the table in the PDF file in the corresponding tab.

Syllabus

Instructors: 

Semester: 

  • Autumn

Year: 

2018

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 3rd 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

October 2018

Mon Tue Wed Thu Fri Sat Sun
1
2
3
4
5
6
7
 
 
 
18:30 to 19:30
 
 
19:45 to 21:00
 
 
 
 
 
8
9
10
11
12
13
14
 
 
 
 
 
 
15
16
17
18
19
20
21
 
 
 
18:30 to 20:00
 
 
 
 
 
22
23
24
25
26
27
28
 
 
 
 
 
 
 
29
30
31
1
2
3
4
 
 
 
 
 
 
 
Materials

Lectures

Titlesort descending Published Short description Files
L01: Introduction 05.09.2018 Introduction to the course L01-Introduction.pdf
L02: Modeling. Linear Programming 12.09.2018 Modeling. Model types. Linear programming. Graphical method. L02-Modeling_Linear_Programming.pdf
L03: Optimization. Convexity 25.09.2018 Optimization. Convexity. Newton's Method. Least Squares. L03-Optimization_Convexity.pdf
L04: Simplex Method 26.09.2018 Simplex Method. Nelder-Mead Method. Problems with bounds and constraints. L04-Simplex_Method.pdf
L05: Linear Systems 03.10.2018 Linear systems. Frequency domain analysis. Basic control design. L05_Linear_systems.pdf

Exercises

Titlesort descending Published Short description Files
L02: Linear Programming Problems 25.09.2018 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
L04: Exercises for Simplex Method 26.09.2018 Exercises for solving LPPs using Simplex Method L04_Exercises_Simplex_Method.pdf
PRJ0: Course project presentation template 12.09.2018 MS Powerpoint slides template for the course project presentation. ISS0031_Project_presentation_slide_template.pptx

Laboratory works

Titlesort descending Published Short description Files
P01: Introduction to MATLAB. Practical optimization. 26.09.2018 Introduction to MATLAB. Newton's method. Least Squares. Lab_01.pdf p01.m
P02: Linear Programming. Nelder-Mead Simplex Method. 26.09.2018 Linear Programming. Nelder-Mead Simplex Method. Lab_02_exercises.pdf optimize.zip p02_ex.m
P03: Linear systems. Control design. 03.10.2018 Linear systems. Control design. Lab_03_CSToolbox_functions.pdf Lab_03_Exercises.pdf p03_01.m
Assignments
Titlesort descending Published Short description Files
*P01: Identification by Linear Models 17.10.2018 Materials for the first reported laboratory work: Identification by linear models R_Lab_01.pdf lab4_datasets.mat p01s.m vplot.m example_ident_session.sid