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Estimation and Decision Systems

Code: M.EEC013     Acronym: ESD

Keywords
Classification Keyword
OFICIAL Automation and Control

Instance: 2021/2022 - 1S Ícone do Moodle

Active? Yes
Responsible unit: Department of Electrical and Computer Engineering
Course/CS Responsible: Master in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
M.EEC 16 Syllabus 2 - 6 39

Teaching language

Portuguese

Objectives

At the end of this course, students are expected to acquire a solid base of basic knowledge to understand
the estimation and identification problems and the methods that today constitute the "state of the art" in these areas, as well as decision making in an environment of uncertainty.

Learning outcomes and competences

At the end of this UC, students are expected to:

1. Understand Stochastic Processes in discrete time.

2. Know deterministic-stochastic transfer function models of discrete-time systems (ARX, ARMAX, Box Jenkins).

3. Know how to plan identification experiments and estimate models from experimental data.

4. Understand Markov Processes and Bayes Networks.

5. Understand Stochastic Dynamic Programming

6. Know how to build and use Markov decision processes

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)


  • Control Theory

  • Algebra

  • Probability and Statistics

Program

Stochastic Processes in discrete time.
Noise models.
Deterministic-stochastic transfer function models of discrete time systems (ARX, ARMAX, Box Jenkins). Prediction, Parameter Estimation and Identification of Discrete Time System Transfer Function Models (Least Squares, Instrumental Variables, and Prediction Error Optimization Methods).
Markov processes. Bayes nets. Inference. Stochastic Dynamic Programming and
Markov Decision Processes. Decision Theory

Mandatory literature

Ljung, Lennart; System identification. ISBN: 0-13-881640-9
Puterman, M. L.; Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. , John Wiley & Sons., 2014

Complementary Bibliography

Verhaegen, Michel and Verdult, Vincent; Filtering and System Identification - A least squares approach, Cambridge University Press, 2007. ISBN: ISBN-13 978-0-521-87512-7
Paulo Jorge de Azevedo Lopes dos Santos; Identificação de sistemas dinâmicos
Lopes dos Santos, Paulo; Perdicoúlis, T-P A; Novara, Carlo; Ramos, Jose; Rivera, Daniel; Linear Parameter Varying Systems - New Developments and Trends, World Scientific, 2012. ISBN: 13-978-981-4355-44-5
Kumar, P. R., & Varaiya, P. ; Stochastic systems: Estimation, identification, and adaptive control. ., SIAM, 2015
Graham C. Goodwin; Adaptive filtering. ISBN: 0-13-004069-X

Teaching methods and learning activities


  • Theoretical Lectures: Subject exposition using slides and the board. 

  • TP Lectures: Execution of Lab works with real or simulated data to demonstrate of concepts. Small Identification, decision and Estimation Projects.

  • Lectures will be of theoretical presentations illustrated with real and simulated examples using MATLAB/Octave
    that clarify the concepts and results presented. Resolution of exercises and implemention of small projects,
    proposed by the lecturers, that encourage an active and critical participation of students. Use of
    computational tools in data processing namely MATLAB/Octave

Software

Octave
Matlab
System Identification Toolbox

keywords

Technological sciences > Engineering > Systems engineering > Systems theory

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Teste 90,00
Participação presencial 10,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 78,00
Frequência das aulas 56,00
Trabalho laboratorial 28,00
Total: 162,00

Eligibility for exams

Do not exceed the absence limit.

Calculation formula of final grade

The students have to do 2 tests. The first one at the middle of the semester and the second one at the end.

They will also have to carry out regular questionnaires.

The final grade is calculated by the formula

final grade=0.5*T1+0.5*T2++0.1*Q

T1 -First Test
T2 - Second teste
Q - questionnaires



 An oral exame is required for those students who want more than 18

Students with less than 10 may take the appeal examination. In this case the final grade will be the one obtained in the examination.

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 witten tests. 

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.

Observations

Students who obtained frequency in the previous year  (only in the previous year) .
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