Advanced System Identification - Modultafeln of TU Ilmenau
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|module properties Advanced System Identification in degree program Diplom Elektrotechnik und Informationstechnik 2017|
|department||Department of Computer Science and Automation|
|ID of group||2211 (Automation Engineering)|
|module leader||Prof. Dr. Yuri Shardt|
|term||winter term only|
|on-campus program (h)||45|
|exam||examination performance with multiple performances|
|details of the certificate||Das Modul Advanced System Identification mit der Prüfungsnummer 220485 schließt mit folgenden Leistungen ab:|
Details zum Abschluss Teilleistung 2:
Pass for labority component
|alternative examination performance due to COVID-19 regulations incl. technical requirements|
|signup details for alternative examinations|
|maximum number of participants|
|previous knowledge and experience||Good understanding of|
statistics and linear regression (such as that offered in the course System
Identification (de: Systemidentification)), calculus, linear algebra, and basic
control (such as that offered in the course Control Engineering I)
By the end of the course, students should be able to analyse and understand the results from modelling and how to improve the model he has obtained. From the lectures, they will have learnt the theoretical foundation of stochastic modelling, Kalman filters, and process identification of time-varying processes. From the laboratories, they will have learnt how to apply stochastic modelling to real examples using appropriate software. From the lectures and laboratories, the students should have learnt how to develop and implement solutions that require the use of statistics, stochastic modelling, and system identification for real-world problems. They should have learnt to constructively take criticism and implement comments and suggestions from their instructors and fellow students.
In this course, the students will learn the following topics:
1. Review of Probability Theory and Regression Analysis
(Chapters 2 and 3)
2. Stochastic Modelling, including the prediction error
model and the Kalman filter (Chapter 5)
3. System Identification of Time-Dependent Systems
|media of instruction and technical requirements for education and examination in case of online participation|
Presentations, Course notes, and Whiteboard lectures, Skype, Moodle
|literature / references|
1. Yuri A.W. Shardt (2015). Statistics for Chemical and
Process Engineers: A Modern Approach, Springer International Publishing:
Cham, Switzerland. (414 pp.) ISBN: 978-3-319-21508-2. doi:
2. Lenart Ljung (1999). System Identification:
Theory for the User, 2nd Edition, Prentice Hall: Englewood Cliffs, New
Jersey, USA. (640 pp.) ISBN: 978-0136566953
|evaluation of teaching|