Modeling and System Identification

Prof. Moritz Diehl, Katrin Baumgärtner, Naya Baslan, Jakob Harzer, Doga Can Öner

 

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.

Course language is English and all course communication is via this course homepage.


Lectures and Q&A sessions. Due to Corona regulations, we provide lecture recordings, which are discussed in a weekly Q&A session held via Zoom. The Q&A sessions take place on Wednesdays 9:00 to 10:00h and are meant to discuss questions about the lecture with Prof. Diehl. Please watch the corresponding lecture recordings beforehand, i.e. stick to the lecture schedule given below.

  • Wednesday, 9:00 to 10:00, Zoom Meeting, Meeting ID: 852 9843 8501, Passcode: MSIQ&A2020

Exercises. The exercise sheets include both pen-and-paper exercises as well as programming exercises using Matlab. Exercise sheets are uploaded to the course webpage on Tuesdays. You have one week to work on the sheet and you might work in groups of at most three students.

Programming exercises should be handed in via Matlab Grader (you'll get an invitation link on Wednesday, October 4th). The pen-and-paper exercises should be uploaded to the Ilias course page. We will also upload solution recordings to the Ilias course webpage.

There are three exercise sessions on Fridays:

  • Friday, 10:00 to 11:00, via Ilias (BigBlueButton)
  • Friday, 11:00 to 12:00, via Ilias (BigBlueButton)
  • Friday, 13:00 to 14:00, via Ilias (BigBlueButton)

During the exercise session, the exercise solutions are discussed. Afterwards there is room for questions on the current exercise sheet.

Written material. The lecture closely follows the script, which can be found here. Please note that we do not cover Chapter 8 and Chapter 9.4. Additional material that covers most of the lecture contents:

  • A script by Johan Schoukens (Vrije Universiteit Brussel, Belgium), which can be found here.
  • The textbook Ljung, L. (1999). System Identification: Theory for the User. Prentice Hall. This book is available in the faculty library.

Final Evaluation and Microexams

Please make sure you register for both the MSI Exam and the MSI Studienleistung!

The final grade of the course is based solely on a final written exam at the end of the semester. The final exam is a closed book exam, only pencil, paper, and a calculator, and two double-sided A4 pages of self-chosen formulae are allowed.

Each exercise sheet gives a maximum of 10 points. Three microexams written during some of the lecture slots give a maximum of 10 exercise points each. In order to pass the exercises accompanying the course (Studienleistung), one has to obtain at least 20 exercise points in each of the three blocks:

  • Block: Exercises 1 - 3 + Microexam 1,
  • Block: Exercises 4 - 6 + Microexam 2,
  • Block: Exercises 7 - 9 + Microexam 3.

Q&A sessions and Microexams

   
Wednesday, November 04, 9:00-10:00 Intro session
Wednesday, November 11, 9:00-10:00 Q&A session on lecture 1, statistics & linear algebra tutorial
Wednesday, November 18, 9:00-10:00 Q&A session on lecture 2 + 3
Wednesday, November 25, 9:00-10:00 Q&A session on lecture 4 + 5
Wednesday, December 02, 9:00-10:00 Microexam 1 on Chapter 1-4.2
Wednesday, December 09, 9:00-10:00 Q&A session on lecture 6 + 7 + 8
Wednesday, December 16, 9:00-10:00 Q&A session on lecture 9 + 10
Wednesday, January 13, 9:00-10:00 Microexam 2 on Chapter 4.2-5.4
Wednesday, January 20, 9:00-10:00 Q&A session on lecture 11 + 12 + 13
Wednesday, January 27, 9:00-10:00 Q&A session on lecture 14 + 15
Wednesday, February 03, 9:00-10:00 Microexam 3 on Chapter 6.3-7, 9-9.3
Wednesday, February 10, 9:00-10:00 Q&A session on lecture 16 + 17

 


 

 Lecture Schedule (Recordings)

      chapters
Friday,  November 6 Lecture 1: Introduction + Resistance Estimation 1-1.2
Wednesday,  November 11 Lecture 2: Resistance Estimation + Statistic Basics 1.2.2-2.3
Friday,  November 13 Lecture 3: Random Variables + Statisitical Estimators 2.3-2.4
Wednesday,  November 18 Lecture 4: Resistance Estimation Revisited 2.5-3.1
Friday,  November 20 Lecture 5: Optimization Basics + Linear Least Squares 3.1-4.2
Wednesday,  November 25 Lecture 6: WLS + Ill-posed Problems 4.3-4.4.1
Friday,  November 27 Lecture 7: Statistical Analysis of WLS 4.5-4.7
Wednesday,  December 02 ---  
Friday,  December 04 Lecture 8: Maximum Likelihood Estimation 5-5.1.1
Wednesday,  December 09 Lecture 9: MAP Estimation + Recursive LLS 5.2-5.3.2
Friday,  December 11 Lecture 10: Cramer Rao Bound 5.3-5.4
Wednesday,  December 16 Lecture 11: Practical Solution of NLS 5.5.-6.2.2
Friday,  December 18 Lecture 12: Dynamic System Classes 6.2.3-6.5.2
Christmas Break    ---  
Friday,  December 15 Lecture 13: Output and Equation Errors 7.1-7.3
Wednesday,  January 20 Lecture 14: State Space Models 7.4
Friday,  January 22 Lecture 15: RLS + Kalman Filter 9.1-9.3
Wednesday,  January 27 Lecture 16: Extended Kalman Filter 9.5
Friday,  January 29 Lecture 17: Moving Horizon Estimation 9.6

Exercises and Tutorials

In the first week, there is no exercise sheet, but if you don't feel too confident about your linear algebra and statisitics skills, you might want to check out these tutorials that cover the basics needed for the MSI course.

  • Linear Algebra Tutorial
  • Statisitics Tutorial

Solution recordings can be found on the ilias course page.

    published on:   due:  
Sheet 1: Linear Algebra Basics + Estimator Example 10. november 18. november, 9 a.m.  
Sheet 2: Statistics + Parameter Estimation 17. november 25. november, 9 a.m.  
Sheet 3: Optimality Conditions + Linear Least Squares 24. november 02. december, 9 a.m.  
Sheet 4 01. december 09. december, 9 a.m.  
Sheet 5 08. december 16. december, 9 a.m.  
Sheet 6 15. december 13. january, 9 a.m.  
Sheet 7 12. january 20. january, 9 a.m.  
Sheet 8 19. january 27. january, 9 a.m.  
Sheet 9 26. january 03. february, 9 a.m.