Course detail
Modelling and Identification
FEKT-MMIDAcad. year: 2012/2013
The subject is oriented on:
- identification methods of dynamic systems
- approaches towards nonparametric and parametric identification
- on-line and off-line identification
- spectral estimation, assessment of noise and disturbance influence on identification results
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Individual project - Max. 15 points.
Final Exam - Max. 70 points.
Course curriculum
2. Nonparametric identification methods
3. Input signal for identification
4. Least squares method
5. Dynamic system models for system identification
6. Recursive LSM
7. Instrumental variable methods
8. Identification methods based on prediction error whitening
9. Practical notes on system identification
10. Identification using neural nets and fuzzy modeling
11. Another approaches to system identificaiton
12. Identification of nonlinear dynamic systems
13. Course summary
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Ljung, L.: System Identification, Theory for the User, Prentice Hall, 1987 (EN)
Noskievič, P.: Modelování a identifikace systémů. Montanex Ostrava 1999 (CS)
Soderstrom T., Stoica P.: System Identification. Prentice Hall International, 1989 (EN)
Šimandl, M.: Identifikace systémů a filtrace. Západočeská univerzita v Plzni, 2001, ISBN 80-7082-170-1. (CS)
Recommended reading
Classification of course in study plans
- Programme EEKR-M Master's
branch M-BEI , 1 year of study, winter semester, elective interdisciplinary
branch M-KAM , 2 year of study, winter semester, elective specialised - Programme EEKR-M Master's
branch M-BEI , 1 year of study, winter semester, elective interdisciplinary
branch M-KAM , 2 year of study, winter semester, elective specialised - Programme EEKR-CZV lifelong learning
branch EE-FLE , 1 year of study, winter semester, elective specialised
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Nonparametric methods of identification.
Linear regression and least squares method.
Useful excitation signals, persistent excitation, pseudorandom binary sequence.
Prediction error method.
Instrumental variable method.
Recursive identification methods, numerically stable identification methods.
Spectral estimation, AR, MA and ARMA models.
Identification in closed loop.
Validation of identified model.
Kalman filter, extended Kalman filter.
Practical notes to identification.
Summary of acquired knowledges about identification of dynamic systems.
Exercise in computer lab
Teacher / Lecturer
Syllabus
Basic methods for nonparametric identification.
Least squares error method.
Generation of excitation signals.
Recursive least squares method.
Influence of noise acting in different places of the system on identification results.
Basic commands from MATLAB Identification Toolbox.
Utilization of MATLAB Identification Toolbox.
Utilization of MATLAB Identification Toolbox.
Spectral estimation of discrete time models.
Experiments with Kalman filter.
Quality evaluation of the identified method.
Exercises evaluation.