Technische Universität Ilmenau

Advanced System Identification - Modultafeln of TU Ilmenau

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module properties Advanced System Identification in degree program Master Elektrotechnik und Informationstechnik 2014 (AST)
module number200127
examination number220485
departmentDepartment of Computer Science and Automation
ID of group 2211 (Automation Engineering)
module leaderProf. Dr. Yuri Shardt
term winter term only
languageEnglisch
credit points5
on-campus program (h)45
self-study (h)105
obligationelective module
examexamination performance with multiple performances
details of the certificateDas Modul Advanced System Identification mit der Prüfungsnummer 220485 schließt mit folgenden Leistungen ab:
  • schriftliche Prüfungsleistung über 120 Minuten mit einer Wichtung von 100% (Prüfungsnummer: 2200815)
  • Studienleistung mit einer Wichtung von 0% (Prüfungsnummer: 2200816)


Details zum Abschluss Teilleistung 2:

Pass for labority component

signup details for alternative examinations
maximum number of participants
previous knowledge and experienceGood 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)
learning outcome

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.

content
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
(Chapter 6)
media of instruction

Presentations, Course notes, and Whiteboard lectures

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:
10.1007/978-3-319-21509-9.


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