Statistical Methods for Analysis and Identification of Mechanical Systems
Code | Completion | Credits | Range |
---|---|---|---|
W31OZ009 | ZK | 26P+52C |
- Course guarantor:
- Michael Valášek
- Lecturer:
- Václav Bauma, Zbyněk Šika, Michael Valášek
- Tutor:
- Václav Bauma, Zbyněk Šika, Michael Valášek
- Supervisor:
- Department of Mechanics, Biomechanics and Mechatronics
- Synopsis:
-
The student will be acquainted with the principles and procedures of statistical methods for analysis and identification of mechanical systems.
Models of dynamic systems: state models, ARX, AR, ARMAX, ARMA, OE, BJ.
Random process and its statistical characteristics
Passing random process through linear dynamic system
Signal processing, sampling, windows
Identification of linear dynamic system, experimental and operational modal analysis
Models of nonlinear dynamic systems: Markov chains, Wiener model
Passing random process through nonlinear dynamic system
Identification of nonlinear dynamic system
Stochastic identification methods: regression, correlation, adaptive methods
Identification of nonlinear systems: neuro-fuzzy methods (LOLIMOT), neural networks.
Kalman filter for nonlinear systems
Identification in closed loop control. Identification of unstable systems.
- Requirements:
- Syllabus of lectures:
- Syllabus of tutorials:
- Study Objective:
- Study materials:
-
Julius S. Bendat, Allan G. Piersol: Engineering Applications of Correlation and Spectral Analysis, J. Wiley 2013
Rolf Isermann, Marco Münchhof : Identification of Dynamic Systems: An Introduction with Applications, Springer 2010
L. Ljung: System Identification - Theory for User, Prentice Hall PTR, 1999
Štefan, M.; Šika, Z.; Valášek, M.; Bauma, V.: Neuro-Fuzzy Identification of Nonlinear Dynamic MIMO Systems
Engineering mechanics. 2006, 13(3), pp. 223-238.
- Note:
- Time-table for winter semester 2024/2025:
- Time-table is not available yet
- Time-table for summer semester 2024/2025:
- Time-table is not available yet
- The course is a part of the following study plans: