**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. Completing the control engineer's identification toolbox, 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, while Zoltan Nagy teaches the lab classes for the English line.

Grading:

- 40%=2x20% two lab tests, see their section below for details.
- 30% final exam.
- 30% project, see below.
- 10% lecture quizzes: a short quiz 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. The lab solutions 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 working, 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. More details about each component (labs, lab tests, project evaluation) will be found at the appropriate places in their separate descriptions below. You can find your current status here.

Dates and locations:

- Lectures: Wednesdays from 12PM in room 356 (Baritiu street). There are 13 lectures in total.
- Labs & project: Mondays between 12PM and 4PM, and Tuesdays between 11AM and 3PM, in rooms C01 and C13 (Dorobantilor street). Everyone will have 11 labs, 2 lab tests, and 5 project sessions.

Due to interdependencies between lectures, labs, and project classes, as well as other reasons such as national holidays, the actual schedule is slightly different from the official one. For instance, during the test weeks, we will need to use some 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 and time slot indicated in the official schedule, so you don't need to worry about changes there.

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).

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.

- 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.
- 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: 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.

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 3.

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).