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Main menu for Browse IS/STAG
Course info
KKY / ISF
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Course description
Department/Unit / Abbreviation
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KKY
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ISF
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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System Identification and Filtration
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
6
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
3
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Summer semester
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0 / -
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6 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
Yes
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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KKY/STP
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The aim of the course is to introduce students to approaches and methods of model building by experimental data- system identification and to approaches and methods concerning state estimation of linear and nonlinear stochastic systems.
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Requirements on student
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Elaboration of four written reports and to understand content of the lectures.
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Content
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1.System identification and mathematical modelling, introduction,
2.System, model structures, experimental conditions, identification methods,
3.Linear regresion, least squares method,
4.General structure of linear stochastic input-output model, special cases,
5.Optimal prediction for linear stochastic system,
6.Prediction error methods,
7.Instrumental variable method, Yule Walker equations,
8.Recursive methods of parameter identification,
9.Nonparametric methods, namely correlation and spectral analysis,
10.Probabilistic modelling, Bayesian approach,filtering, prediction, smoothing, point estimates,
11.Linear stochastic systems, Kalman filtering and Bayesian recursive relations,
12.State estimation of nonlinear stochastic systems-local methods (extended, unscented, difference Kalman filters),
13.State estimation of nonlinear stochastic systems-global methods (point-mass, particle, Gaussian sum filters).
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Activities
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Fields of study
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Studentům předmětu jsou k dispozici prezentace a nahrané přednášky sdílené přes skupinu v MS Teams. K dispozici jsou rovněž skripta předmětu v elektronické podobě.
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Guarantors and lecturers
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Literature
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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39
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Preparation for an examination (30-60)
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45
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Graduate study programme term essay (40-50)
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50
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Practical training (number of hours)
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26
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Total
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160
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
aplikovat metody lineární algebry |
aplikovat základní techniky integrálního a diferenciálního počtu |
popsat vlastnosti náhodných veličin, stochastických procesů a stochastických systémů |
interpretovat stavový a vstupně-výstupní popis systému |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
aplikovat metody lineární algebry při analýze vlastností lineárně transformované náhodné veličiny |
aplikovat techniky integrálního a diferenciálního počtu při práci s náhodnými veličinami |
převést vstupně-výstupní popis systému na stavový a naopak |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
analyzovat a vyhodnotit vlastnosti odhadu stavu i parametrů |
formulovat problém odhadu neznámých parametrů na základě dostupných dat |
formulovat problém odhadu stavu stochastických dynamických systémů |
vybrat vhodnou filtrační techniku pro danou formulaci problému odhadu stavu |
vybrat vhodnou identifikační techniku pro danou formulaci problému odhadu parametrů |
Skills - skills resulting from the course: |
navrhnout a aplikovat metodu nejmenších čtverců, metodu chyby predikce a metodu přídavné proměnné pro odhad parametrů vstupně-výstupních modelů |
navrhnout a aplikovat metody globální filtrace pro odhad stavu stochastického dynamického systému (zejména metoda vícenásobné linearizace a metoda bodových mas) |
navrhnout a aplikovat metody lokální filtrace pro odhad stavu stochastického dynamického systému (zejména rozšířený Kalmanův filtr a unscentovaný Kalmanův filtr) |
Competences - competences resulting from the course: |
N/A |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Oral exam |
Written exam |
Skills - skills achieved by taking this course are verified by the following means: |
Seminar work |
Competences - competence achieved by taking this course are verified by the following means: |
Oral exam |
Seminar work |
Written exam |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Self-study of literature |
Practicum |
Skills - the following training methods are used to achieve the required skills: |
Practicum |
Competences - the following training methods are used to achieve the required competences: |
Lecture |
Practicum |
Self-study of literature |
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