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Course info
KMA / 6FUM1
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Course description
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Department/Unit / Abbreviation
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KMA
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6FUM1
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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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Fuzzy Modelling and Application
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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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Written
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Type of completion
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Written
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Time requirements
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lecture
2
[Hours/Week]
practical class
2
[Hours/Week]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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English
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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1 / -
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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 / 0
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0 / 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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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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not determined
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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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English
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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 |
A|B|C|D|E|F |
| Periodicity |
every year
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| Specification periodicity |
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Fundamental theoretical course |
Yes
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| Fundamental course |
No
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| Fundamental theoretical course |
Yes
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| Evaluation scale |
A|B|C|D|E|F |
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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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N/A
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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 basic principles of fuzzy modelling that have lots of practical applications. The education will include a demonstration of a solution of particular problems with help of specialized software developed at the University of Ostrava.
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Requirements on student
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The course is finished by an oral exam. A student can obtain max. 70 points from the exam. A student may obtain further 30 points from a semestral project.
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Content
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1.-4. Basic notions of the theory of fuzzy relations. Fuzzy IF-THEN rules and the related principles. Fuzzy IF-THEN rules as fuzzy relations. The use of fuzzy relational IF-THEN rules and appropriate defuzzifications.
5.-10. Theory of evaluative linguistic expressions. Fuzzy IF-THEN rules as a linguistic description and as a text of the natural language. Possible use of linguistic descriptions. Motivation and principles of fuzzy control. Types of fuzzy controllers, the methodology of their design. Software system LFLC for fuzzy modelling.
11.-12. Takagi-Sugeno IF-THEN rules and their use. Fuzzy cluster analysis.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
Novák, V., Perfilieva, I., Dvořák, A. Insight Into Fuzzy Modeling. John Wiley & Sons, 2016. ISBN 9781119193180.
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Extending:
Michels, K., Klawonn, F., Kruse, R., Nürnberger, A. Fuzzy Control. Fundamentals, Stability and Design of Fuzzy Controllers. Springer, Heidelberg, 2006. ISBN 9783540317661.
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Recommended:
Novák, V., Perfilieva, I., Dvořák, A. Imprecise modeling. World Scientific, Singapore, 2009.
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On-line library catalogues
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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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Source stuying
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20
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Being present in classes
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52
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Semestral work
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30
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Consultation of work with the teacher/tutor (incl. electronic)
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20
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Preparation for an exam
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20
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Self-tutoring
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20
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Total
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162
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Prerequisites
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Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
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| A student knows basics of linear algebra, and of the differential and integral calculus. |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
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| A student understands basic notions from the fuzzy modelling area, understands fuzzy rule-based systems, knows basic approximation theorems, knows types of controllers and understands the principles of fuzzy control. |
Skills - skills resulting from the course: |
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| A student can apply linguistic control for distinct types of fuzzy controllers (P, PD, PI, PID), can apply basic fuzzy clustering analysis fuzzy c-means for the generation of a fuzzy partition of the input universe, can generate Takagi-Sugeno rules from data. |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
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| Continuous analysis of student´s achievements |
| Oral examination |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
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| Dialogic (discussion, dialogue, brainstorming) |
| Monologic (explanation, lecture, briefing) |
| Projection (static, dynamic) |
| Working with text (coursebook, book) |
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