Identification and Estimation
Keywords |
Classification |
Keyword |
OFICIAL |
Automation, Control & Manufacturing Syst. |
OFICIAL |
Basic Sciences for Electrotechnology |
Instance: 2009/2010 - 1S
Cycles of Study/Courses
Acronym |
No. of Students |
Study Plan |
Curricular Years |
Credits UCN |
Credits ECTS |
Contact hours |
Total Time |
MIEEC |
26 |
Syllabus since 2007/2008 |
5 |
- |
6 |
60 |
162 |
Teaching language
Portuguese
Objectives
- To acquire the theoretical basis for understanding the estimation and identification problems as well as the methods that today constitute the "state of the art" in this area.
- To know the different approaches to the system identification problem with emphasis on the
Program
Part I - Introduction to stochastic processes and Kalman filter
Part II - System Identification Theorectical tools
- Complements of linear algebra
- Input-output and state space models of Linear Time Invariant Systems
Part III - Stochastic processes and Kalman filter
- Stationary and ergodic processes
- Spectral analysis
- White Noise
- Input-Output models
- Input-Output predictive models
- State-space models
- The Kalman filter
- The innovation model
Part IV - System Identification methods
- Least Squares Estimator
- Impulse response estimation
-Input-Output models estiamtion
- Deterministic realization theory.
- Subspace Identification methods
Mandatory literature
Katayama, Tohru; Susbspace Methods for System Identification, Springer-Verlag , 2005. ISBN: 1852339810
Lopes dos Santos, Paulo; Tópicos de Álgebra Linear, 2007
Lopes dos Santos, Paulo; Processos Estocásticos e Filtro de Kalman, 2007
Ljung, Lennart;
System identification. ISBN: 0-13-881640-9
Lopes dos Santos, Paulo; Sistemas Lineares e Invariantes no Tempo ("Lecture Notes escritas para esta disciplina), 2007
Complementary Bibliography
Van Overschee, Peter;
Subspace identification for linear systems. ISBN: 0-7923-9717-7
Delgado, Catarina Judite Morais;
Identificação no subespaço de estados de sistemas lineares
Teaching methods and learning activities
Theoretical Lectures: Subject exposition using slides and the board.
TP Lectures: Problems resolution. Concepts Demonstration of concepts with real or simulated data. Small Identification and Estimation Projects.
Software
System Identification Toolbox - Release 11
Matlab 6
keywords
Technological sciences > Engineering > Systems engineering > Systems theory
Evaluation Type
Distributed evaluation with final exam
Assessment Components
Description |
Type |
Time (hours) |
Weight (%) |
End date |
Subject Classes |
Participação presencial |
72,00 |
|
|
Exam |
Exame |
3,00 |
|
|
|
Total: |
- |
0,00 |
|
Amount of time allocated to each course unit
Description |
Type |
Time (hours) |
End date |
Self-study |
Estudo autónomo |
40 |
|
Final exam preparation |
Estudo autónomo |
20 |
|
|
Total: |
60,00 |
|
Eligibility for exams
Do not exceed the absence limit.
Calculation formula of final grade
Exam + Distributed evaluation.
Ordinary students
Exam: 15
Distributed evaluation: 5
Any student with a final grade greater or equal to 18 may require an oral exam for possible assignment of a grade higher than 18.
Special assessment (TE, DA, ...)
The students admitted to the examination because they were released from attending the lectures (according to points a) b) c) of Article 4 of the General Evaluation Rules), will make the ordinary students normal witten examinations.
The Working students will only be graded by the final examination.
Classification improvement
The classification improvement can be made in special examination period . The examination grade will be the final grade if better than the previous one.