Duration:
1 Semester | Turnus of offer:
irregularly in the summer semester | Credit points:
4 |
Course of studies, specific field and terms: - Master MES 2020 (optional subject), computer science / electrical engineering, Arbitrary semester
- Master MES 2014 (optional subject), computer science / electrical engineering, Arbitrary semester
- Master Robotics and Autonomous Systems 2019 (optional subject), Additionally recognized elective module, 1st or 2nd semester
|
Classes and lectures: - System Identification (exercise, 1 SWS)
- System Identification (lecture, 2 SWS)
| Workload: - 45 Hours in-classroom work
- 10 Hours exam preparation
- 65 Hours private studies and exercises
| |
Contents of teaching: | - Introductory topics:
- Discretization and Discrete-time (DT) models
- Least-square estimation
- Main topics:
- Parametric model identification: Prediction error method, Subspace identification
- Non-parametric model identification
- Data-driven models
- Model Validation
| |
Qualification-goals/Competencies: - The students can explain the general framework and basic properties of different identification methods including least-squares method, the prediction error method, the subspace method, standard non-parametric methods and the data-driven method.
- Students can formulate and implement algorithms for system identification.
- students are able to estimate mathematical models of a dynamical system from input-output data using the different methods presented in this course.
- They can evaluate the quality of the identified models.
- They can use Matlab System Identification Toolbox to identify linear dynamical models using different identification methods.
|
Grading through: - Written or oral exam as announced by the examiner
|
Responsible for this module: Teachers: - Dr.-Ing. Hossameldin Abbas
|
Literature: - Karel J. Keesman: System Identification: An Introduction - Springer-Verlag London Limited 2011
- Lennart Ljung and Torkel Glad: Modeling of Dynamic Systems - Prentice Hall 1994
- Lennart Ljung: System Identification - Theory for the User - Prentice Hall 1999
|
Language: |
Notes:Admission requirements for taking the module: - None Admission requirements for participation in module examination(s): - none Module Exam(s): - CS4480-L1: System Identification, Oral Examination, 100% of module grade |
Letzte Änderung: 31.8.2022 |
für die Ukraine