Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Szkoła Doktorska - ZUT Doctoral School

Sylabus przedmiotu Optimization and identification methods:

Informacje podstawowe

Kierunek studiów ZUT Doctoral School
Forma studiów studia stacjonarne Poziom
Stopnień naukowy absolwenta doktor
Obszary studiów charakterystyki PRK
Profil
Moduł —
Przedmiot Optimization and identification methods
Specjalność IT, ELECTRICAL ENGINEERING AND MECHANICAL ENGINEERING BLOCK
Jednostka prowadząca Katedra Technologii Wytwarzania
Nauczyciel odpowiedzialny Bartosz Powałka <Bartosz.Powalka@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 0,5 ECTS (formy) 0,5
Forma zaliczenia zaliczenie Język angielski
Blok obieralny 8 Grupa obieralna 1

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW5 8 0,51,00zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Linear algebra
W-2Iterative methods
W-3Calculus

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Ability to identify parameters of mathematical models
C-2Determine the uncertainty of identified parameters.
C-3Model order selection.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
wykłady
T-W-1General identification algorithm. The concept of model validation.2
T-W-2The least squares method . Orthogonal least squares method. Application of SVD techniques. Regularization techniques. Methods of selecting the regularization parameter.2
T-W-3Nonparametric identification. Estimation of spectral density functions of signals. Construction of AR, ARMA models. Selection of model order.2
T-W-4Methods of optimization. Necessary and sufficient conditions for the existence of an extremum. Gradient and gradient-free methods. Lagrange multipliers. Kuhn-Tucker condition. Linear programming. Nonlinear programming.2
8

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
wykłady
A-W-1Class participation8
A-W-2Exam preparation5
A-W-3Participation in consultations2
15

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Informative lecture
M-2Computer class

Sposoby oceny

KODSposób oceny
S-1Ocena podsumowująca: Examination of the ability to formulate identification and optimization tasks and knowledge of concepts.
S-2Ocena formująca: Evaluation based on tasks solved in class and homework assignments

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
ISDE_4-_IEM06.1_W01
Student has a knowledge in the field of programming, statistics and optimization.		ISDE_4-_W02, ISDE_4-_W03—C-1T-W-1, T-W-2, T-W-3, T-W-4M-1S-1

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
ISDE_4-_IEM06.1_K01
The sutudent has the ability to confront his own original solutions with methods described in the scientific literature. He is also able to adapt these methods to solve problems of his scientific discipline.		ISDE_4-_K01, ISDE_4-_K02—C-1, C-2T-W-2, T-W-3, T-W-4M-2S-2

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
ISDE_4-_IEM06.1_W01
Student has a knowledge in the field of programming, statistics and optimization.		2,0
3,0The student has the ability to formulate and solve problems on identification of mathematical models.
3,5
4,0
4,5
5,0

Kryterium oceny - inne kompetencje społeczne i personalne

Efekt uczenia sięOcenaKryterium oceny
ISDE_4-_IEM06.1_K01
The sutudent has the ability to confront his own original solutions with methods described in the scientific literature. He is also able to adapt these methods to solve problems of his scientific discipline.		2,0
3,0Student potrafi oceniać jakość zidentyfikowanego modelu w stopniu dostatecznym
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. S. Rao, Engineering optimization. Theory and practice., Wiley, 2009

Literatura dodatkowa

  1. Luenberger, Teoria optymalizacji, PWN, Warszawa, 1974

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1General identification algorithm. The concept of model validation.2
T-W-2The least squares method . Orthogonal least squares method. Application of SVD techniques. Regularization techniques. Methods of selecting the regularization parameter.2
T-W-3Nonparametric identification. Estimation of spectral density functions of signals. Construction of AR, ARMA models. Selection of model order.2
T-W-4Methods of optimization. Necessary and sufficient conditions for the existence of an extremum. Gradient and gradient-free methods. Lagrange multipliers. Kuhn-Tucker condition. Linear programming. Nonlinear programming.2
8

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Class participation8
A-W-2Exam preparation5
A-W-3Participation in consultations2
15
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_IEM06.1_W01Student has a knowledge in the field of programming, statistics and optimization.
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_W02They have extended, theory-based knowledge relating to the represented field and discipline and detailed knowledge at an advanced level in the area of scientific research ,methodology of scientific work, preparation of publications and presentations of research results and the principle of dissemination of the results of scientific work, including open access mode.
ISDE_4-_W03They know and understand fundamental dilemmas of modern civilisation, also in relation to the recent scientific developments in the represented field and discipline.
Cel przedmiotuC-1Ability to identify parameters of mathematical models
Treści programoweT-W-1General identification algorithm. The concept of model validation.
T-W-2The least squares method . Orthogonal least squares method. Application of SVD techniques. Regularization techniques. Methods of selecting the regularization parameter.
T-W-3Nonparametric identification. Estimation of spectral density functions of signals. Construction of AR, ARMA models. Selection of model order.
T-W-4Methods of optimization. Necessary and sufficient conditions for the existence of an extremum. Gradient and gradient-free methods. Lagrange multipliers. Kuhn-Tucker condition. Linear programming. Nonlinear programming.
Metody nauczaniaM-1Informative lecture
Sposób ocenyS-1Ocena podsumowująca: Examination of the ability to formulate identification and optimization tasks and knowledge of concepts.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student has the ability to formulate and solve problems on identification of mathematical models.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_IEM06.1_K01The sutudent has the ability to confront his own original solutions with methods described in the scientific literature. He is also able to adapt these methods to solve problems of his scientific discipline.
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_K01They understand the necessity and are prepared to critically analyse the achieved scientific output and the contribution of the results of their own research activity to the development of the represented field and discipline.
ISDE_4-_K02They understand the obligation to seek creative solutions to the challenges of civilisation, in particular to social, research and creative commitments, are aware of the need to initiate actions in the public interest, to think in the entrepreneurial manner and the need for scientific development for new phenomena and problems in the represented field and discipline.
Cel przedmiotuC-1Ability to identify parameters of mathematical models
C-2Determine the uncertainty of identified parameters.
Treści programoweT-W-2The least squares method . Orthogonal least squares method. Application of SVD techniques. Regularization techniques. Methods of selecting the regularization parameter.
T-W-3Nonparametric identification. Estimation of spectral density functions of signals. Construction of AR, ARMA models. Selection of model order.
T-W-4Methods of optimization. Necessary and sufficient conditions for the existence of an extremum. Gradient and gradient-free methods. Lagrange multipliers. Kuhn-Tucker condition. Linear programming. Nonlinear programming.
Metody nauczaniaM-2Computer class
Sposób ocenyS-2Ocena formująca: Evaluation based on tasks solved in class and homework assignments
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student potrafi oceniać jakość zidentyfikowanego modelu w stopniu dostatecznym
3,5
4,0
4,5
5,0