**Lecturer: Lucian Busoniu, TA: Zoltan Nagy**

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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, 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 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, who also teaches the project classes, while Zoltan Nagy teaches the lab classes for the English line.

Grading:

- 30%=2x15% two lab tests, see their section below for details.
- 10% lab quizzes: a short quiz with two questions, at the start of each lab except the first, from the material relevant to that lab.
- 30% final exam.
- 30% project (15% part 1 + 15% part 2), see below for details.
- 10% lecture quizzes: a short quiz with three questions, at the end of each lecture except the first, from the material discussed during that particular lecture.

The lab solutions, the two lab tests, and a project solution are all required before being admitted to the exam. Note that while the lab solutions are not graded, they are still required; they are validated by the TA during the lab. It is therefore 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. At most two labs can be recovered at the end of the semester; hence, accumulating three or more missing labs means you can no longer become eligibile for the exam during the current year.

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

Dates and locations:

- Lectures: Wednesdays from 12PM, in room 356, Baritiu street.
- Labs & project: Mondays and Tuesdays in rooms C01 (labs) and C13 (projects), Dorobantilor street. Everyone will have 11 labs, 2 lab tests, 4 project sessions, and one project presentation.

Due to interdependencies between lectures, labs, and project classes, as well as other reasons, the actual schedule is slightly different from the official one. For instance, during the test weeks, we will need to use project slots for the test as well, so that everyone has time to take the test. Please look at the schedule carefully to determine exactly when you should be in class.

Everything takes place in the room indicated in the official schedule. For the labs and lectures, the official timeslot is always kept. For the project, sometimes we need to rearrange odd/even weeks (but the hour never changes). Specifically, the labels "odd" and "even" in the table refer to the official table, so "odd" includes 1.2 (group 1 halfgroup 2), 2.1, and 3.1, while "even" includes 1.1, 2.2, and 3.2. You should come to the project class when you see your label in the table; for example, even though week 7 (11-15 November) is odd, since the table says "3 even", all the even halfgroups (1.1, 2.2, and 3.2) should come to the project class during that week and work on project session 3. All this should be feasible given your current schedule; if incompatible changes occur, we will revisit the planning.

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.

- Part 1: Introduction to System Identification (PDF).
- Part 2: Transient Analysis of Step and Impulse Responses (PDF).
- Part 3: Mathematical Background: Linear Regression and Statistics (PDF).
- Part 4: Correlation Analysis (PDF).
- Part 5: ARX Identification (PDF).
- Part 6: Input Signals (PDF).

At the end of each lecture except the first, a 5-minute quiz with three questions is given from that lecture. So, you should pay attention during the lectures. If you answer all the questions 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:

- T. Soderstrom and P. Stoica.
*System Identification*. Prentice Hall, 1989. The full text of this book is available at: http://user.it.uu.se/~ts/bookinfo.html. This book forms the basis of the course. - L. Ljung,
*System Identification: Theory for the User*, 2nd ed., Prentice Hall, 1999. An advanced graduate-level textbook.

Each lab except the first starts with a 3-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.

- Lab 1: (Re)Introduction to Matlab (PDF) -- two short exercises in order to get re-acquainted with Matlab. For a brief intro to Matlab, have a look at the following document (with thanks to Paula Raica for allowing us to use it) ST_Lab1.pdf. You may also use the automated Matlab tutorial, with thanks to Tassos Natsakis who developed it. Note: you may also use the new version of the tutorial.
- Lab 2: Transient Analysis of Step Responses (PDF). The data files are: for first-order systems, #1, #2, #3, #4, #5, #6, #7, #8 (where # stands for index). For second-order systems, #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 3: Transient Analysis of Impulse Responses (PDF). The data files are: for first-order systems, #1, #2, #3, #4, #5, #6, #7, #8. For second-order systems, #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 4: Linear Regression for Function Approximation (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8, #9, #10, #11, #12, #13, #14, #15, #16.
- Lab 5: Correlation analysis (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8.
- Lab 6: ARX Identification (PDF). The data files are: #1, #2, #3, #4, #5, #6, #7, #8.

To be detailed closer to the test.

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; this year we talk about it in lecture 4.

Comments, suggestions, questions etc. related to this course or website are welcome; please contact either the lecturer or the TA at the addresses below (given as images for spambot protection).