4 ECTS credits
110 h study time
Offer 1 with catalog number 4005030ENR for all students in the 1st semester at a (E) Master - advanced level.
Content
This course describes the various steps one has to go through for obtaining a linear dynamic model of a process. It starts with the choice (design) of the measurement setup, the choice (design) of the excitation signal, the choice of the parametric model (discrete time, continuous time, parametric versus non-parametric noise model...), the estimation of the parametric model (identification toolboxes in Matlab), till finally the model selection and the model validation. Hereby the influence of each error source (stochastic measurement errors, systematic measurement errors, non-linear distortions, time-variant effects, model errors...) on the final result is studied in detail.
Each step is illustrated thoroughly by means of real life examples.
The course starts with the basic techniques necessary to predict the stochastic behaviour of an estimator (consistency, bias, efficiency, probability density function, robustness). Furthermore, the continuous thread throughout the entire course is the formulation of the maximum likelihood solution starting from the measured data. This approach has the advantage that provides the estimated model with an estimate of its uncertainty.
Contents
Aims
After following this course, the student should be able to solve independently an identification problem. More particularly, the student should be able to:
In addition, the student must have acquired insight into:
Only participants to the Doctoral Spring School organised by de Department of Electricity can register for version IR-ELEC-7960a of this course.
This version in taught in English. L. Ljung (1999). System Identification: Theory for the User. Prentice-Hall: Upper Saddle River.
R. Pintelon and J. Schoukens (2012). System identification: A Frequency Domain Approach. 2nd edition, IEEE Press: New York.
+ recent papers from top ranked international journals on system identification
AIMS AND OBJECTIVES
This course describes the various steps one has to go through for obtaining a linear dynamic model of a process. It starts with the choice (design) of the measurement setup, the choice (design) of the excitation signal, the choice of the parametric model (discrete time, continuous time, parametric versus non-parametric noise model...), the estimation of the parametric model (identification toolboxes in Matlab), till finally the model selection and the model validation. Hereby the influence of each error source (stochastic measurement errors, systematic measurement errors, non-linear distortions, time-variant effects, model errors...) on the final result is studied in detail. Each step is illustrated thoroughly by means of real life examples.
FINAL REQUIREMENTS
- Knowledge and insight: being able to analyse existing estimators and understand their stochastic behaviour; being able to design an identification experiment; being able to construct an estimator for a given identification problem.
- Opinion formation: for all topics mentioned above being able to solve simple exercises and to make choices; understanding the pros and cons of the choices made; critical attitude w.r.t. to the results obtained.
- Learning skills: using the existing identification literature, being able to solve a practical identification problem (experiment design, choice model, choice estimator, model validation, and uncertainty calculation).
- Communication: clear and accurate oral and written reporting
EXAM REQUIREMENTS
Written report and oral presentation of an identification project. In this project the student should (i) be able to learn new theories/identificationmethods from the literature, (ii) be able to solve a practical identification problem, and (iii) have a crictical attitude w.r.t. the literature and the own results obtained.
This course contributes to the following programme outcomes of the Master in Electronics and Information Technology Engineering:
The Master in Engineering Sciences has in-depth knowledge and understanding of
3. the advanced methods and theories to schematize and model complex problems or processes
The Master in Engineering Sciences can
4. reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
6. correctly report on research or design results in the form of a technical report or in the form of a scientific paper
11. think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information
The Master in Engineering Sciences has
13. a critical attitude towards one’s own results and those of others
15. the flexibility and adaptability to work in an international and/or intercultural context
16. an attitude of life-long learning as needed for the future development of his/her career
The Master in Electronics and Information Technology Engineering:
18. Has a profound knowledge of either (i) nano- and opto-electronics and embedded systems, (ii) information and communication technology systems or (iii) measuring, modelling and control.
21. Is able to model, simulate, measure and control electronic components and physical phenomena.
The final grade is composed based on the following categories:
Oral Exam determines 100% of the final mark.
Within the Oral Exam category, the following assignments need to be completed:
Written report and oral defense of the project. During the oral examination the student can use his/her written report, de course notes, and the available system identification literature.
This offer is part of the following study plans:
Master of Electronics and Information Technology Engineering: Standaard traject (only offered in Dutch)
European Master of Photonics: Standaard traject
Master of Electrical Engineering: Standaard traject BRUFACE J