System Identification 2021-2022

Lecturer: Lucian Busoniu, TAs: Mihalis Maer, Matthias Rosynski, Bilal Yousuf

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About this course

The course introduces nonparametric methods for system identification, as well as parametric methods including prediction error and instrumental variables techniques. Input signals, online recursive methods, closed-loop identification, and model validation are also discussed. The material is described at an appropriate BSc level, and builds in a self-contained manner the required mathematical background. The course is based on the book System Identification by Soderstrom and Stoica.

This year, we will use a mix of offline teaching and online platforms: Microsoft Teams for interacting with students, and ClassMarker to take the quizzes. As a unique ID for each student, please use an email address that is associated to a ClassMarker account. Microsoft Teams can be associated with the different, Didatec email address. Details on how we are using these platforms can be found under the specific type of activity below.

This course is part of the Bachelor program of the Automation Department, UTCluj (3rd year 1st semester). Prerequisites: linear dynamical systems and linear algebra. The lecturer is Lucian Busoniu. Mihalis Maer and Lucian teach the projects, while Matthias Rosynski and Bilal Yousuf teach the lab classes.

Evaluation rules

Grading:

The final grade will be a rounded, weighted average of all the evaluations per the percentages above, capped to 10. Note that each student can accumulate up to 110%x10 = 11 points; the extra point means that you can compensate across categories to a certain extent.

Eligibility conditions: Solutions to all labs except the first one; the two lab tests; and solutions to both parts of the project are all required before being admitted to the exam. More details about each component (labs, lab tests, project evaluation) will be found at the appropriate places in their separate descriptions below.

You can check your current status in the Google sheet table here, updated in near-real-time.

Schedule

Lectures are Wednesdays from 8AM, labs and projects on Mondays and Tuesdays. Please look at the schedule below carefully to determine exactly when you should be in class, and see the overall schedule on http://www.aut.utcluj.ro/ for when your time slots are allocated.

Image with schedule table

Due to interdependencies between lectures, labs, and project classes; as well as lab tests and project deadlines, the actual schedule is slightly different from the official one. The following changes are performed:

Lectures

The lecture slides are mandatory reading; they will be written down in detail to give a self-contained, complete picture of the topics. They are made available here in time for each lecture.

At a random point during a lecture, a short quiz will be given from the material of that lecture, using the ClassMarker platform. A list of email addresses will be collected beforehand and you will be invited to ClassMarker. The quizzes are graded! So, you should pay attention during the lectures. If you answer all the questions in every lecture correctly, you get 1 point in your grade; lower scores scale linearly to between 0 and 1 points in the grade.

In addition to the slides, followers may optionally consult the following books:

Labs

Logistics and platform

Labs will be developed using Matlab. PDF descriptions of the labs are given on this website.

Lab solutions are required starting from lab 2. It is not enough to be present at the lab in order to validate it; a complete, working, and original solution must be developed during the lab. Whether the solution is working will be verified by the teacher during the lab. Once this has been verified, you submit the solution via a dropbox link. Originality will then be verified as described below. At most two labs total can be recovered before the exam, including at most one copied lab; hence, accumulating three or more missing labs means you can no longer become eligibile for the exam.

Half-group boundaries are strict, for example you cannot access the quiz to the lab unless you are present during the lab slot of your halfgroup.

Lab quiz

Each lab except the first starts with a 2-minute quiz containing 2 short questions, which tests you on the material relevant to that particular lab. If you answer everything correctly for all the labs, you get 1 point in your grade; lower scores scale linearly, as for the lecture quizzes. So, you should arrive prepared.

Originality check

The solutions to each lab are run through an automated plagiarism check. Each suspicious case is examined by the teachers, and if it becomes clear that the lab is copied, both the source and the destination lab are invalidated. In addition, we are imposing a "two strikes - you're out" rule. Namely, the first lab copied is only invalidated, and you can recover it at the end. The second lab copied invalidates the full set of solutions, you forfeit the discipline and have to take it next year.

Lab descriptions

Lab tests

Lab test 1: The slots will be assigned in the online table. Please check carefully as due to scheduling constraints some students are out of their regular slot (reassignments are done preventing overlaps with other activities). This is mainly because there are a lot of students and delays after a half-group's lab slot are likely. Keep in mind that the lab tests are required for eligibility. The test will have three components, described and graded as follows:

  1. 3 points: a ClassMarker quiz during the lecture on November 10th, from the material relevant to the test. So you have to attend the lecture, otherwise, you will miss these points.
  2. 5 points: code for redoing a (part of a) lab between 2 and 5, i.e., transient analysis, linear regression, or correlation analysis.
  3. 2 points: one-on-one question(s), both on the code and testing the higher-level insight about the methods in the labs. Bonus points are possible if you demonstrate that you understand the topics significantly above the expected level.

Project

See the project description (PDF) for the topic, rules, and deadlines. Please read it carefully. A detailed description of the linear regression method needed in the first part of the project can be found in last year's lecture; we will talk about it in lecture 4. Alternately, here is a one-page (handwritten) summary.

Guidelines for writing your report and developing and giving your presentation are also available.

Exam

Information will follow.

Contact

Comments, suggestions, questions etc. related to this course or website are welcome; please contact either the lecturer or the TAs via Teams.