Prof. Dr. Moritz Diehl
Modeling and System Identification (MSI) is concerned with the search for mathematical models for real-life systems. The course is based on statistics, optimization and simulation methods for differential equations. The exercises will be based on pen-and-paper exercises and computer exercises with MATLAB.
- Robin Verschueren
- Rachel Leuthold
- Tobias Schöls
- Mara Vaihinger
Lectures take place on Mondays 8:15 to 10:00 and Fridays 10:15 to 12:00, in SR 00-010/014 in building 101.
Course material is the following:
- MSI script by prof. Diehl,
- Script by prof. Johan Schoukens, VUB, Brussels, Belgium,
- Textbook, Ljung, L. (1999). System Identification: Theory for the User. Prentice
Hall. Available in the campus library.
The exam takes place on Monday, March 20th, 2017 form 9:00 to 11:30, in G.-Köhler-Allee 101, Lecture Hall 026 and 036.
This is a closed book exam, only pens, a calculator, and four single A4 pages of self-chosen formulae are allowed. We will provide you with as much paper as you need.
Exercise sessions are organized on (starting in the second week of the semester):
- Tuesday 12:00 to 14:00, building 082, room 029
- Wednesday 12:00 to 14:00, building 101, room 01 016
- Thursday 14:00 to 16:00, building 074, room 019 (IMTEK pool).
Please hand in solutions on paper, and send MATLAB code to email@example.com. (Submissions without a paper solution will not be corrected.)
Please note that copied solutions will receive zero points. Discussion between groups is acceptable if the names of the collaborating group-members are acknowledged on the solution.
|Exercise 0||Monday 24 October, 8:15||Introduction||4 bonus|
|Exercise 1||Dataset 1||Monday 31 October, 8:15||Introduction to Statistics||13 points|
|Exercise 2||Dataset 2||Monday 7 November, 8:15||Linear Least Squares||4 points|
|Exercise 3||Monday 14 November, 8:15||Linear Least Squares||10 points|
|Exercise 4||Dataset 4||Monday 21 November, 8:15||Weighted Least Squares||11 points|
|Exercise 5||Monday 5 December, 8:15||Single-Experiment Least Squares||13 points|
|Exercise 6||Monday 12 December, 8:15||Maximum Likelihood Estimation||10 points|
|Exercise 7||Monday 19 December, 8:15||Variance Estimation and Introduction to Recursive Least Squares||12 points + 3 bonus|
|Exercise 8||Dataset 8||Tuesday 10 January, 10:00||Recursive Least Squares||11 points|
|Exercise 9||Dataset 9||Monday 23 January, 8:15||Parameter Estimation for Dynamic Systems||11 points + 2 bonus|
|Exercise 10||Dataset 10||Monday 30 January, 8:15||Frequency Domain Identification||12 points|
|Exercise 11||Dataset 11||Monday 6 Ferbruary, 8:15||Kalman Filter||3 points + 3 bonus|
A minimum FINAL SCORE of 50% is required to be admitted for the exam.
Lecture Slot Modifications and Cancellations
- Monday Oct 17, 2016: no lecture, but MATLAB tutorial (taught by Rachel) pdf matlab
- Monday Oct 24, 2016 no lecture, but linear algebra tutorial (taught by Tobias) pdf matlab | solution
- Monday Oct 31, 2016 no lecture, but statistics tutorial (taught by Mara) pdf matlab | solution
- Friday Nov 25, 2016: no lecture (feel invited to attend the mini symposium on numerical optimization link)
- Monday Nov 28, 2016: Microexam 1 and discussion of solution
- Friday Dec 9, 2016: no lecture, but voluntary question and answer session (by Rachel)
- Friday Dec 23, 2016: no lecture
- Friday Jan 13, 2017: Microexam 2 and discussion of solution
- Monday Feb 6, 2017: Microexam 3 and discussion of solution
All students must have MATLAB installed on their personal laptop and bring it to the exercise sessions (and the tutorials in the beginning of the semester). The university provides licences.
There is an online (in browser) version of MATLAB. This service is provided by MathWorks and can be accessed with a MathWorks account. We won't be able to provide support for the online version and the exercises may exceed it's capabilities.