Title:

# Fuzzy Systems for Control and Modelling

Code:FSY
Ac.Year:ukončen 2007/2008
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
IT-MSC-2MGM.-Elective
IT-MSC-2MIN.-Elective
IT-MSC-2MIS.-Elective
IT-MSC-2MPS-Elective
Language of Instruction:Czech
Credits:5
Completion:credit+exam (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600026
ExamsTestsExercisesLaboratoriesOther
Points:55150030
Guarantor:Jura Pavel, prof. Ing., CSc. (DAME)
Faculty:Faculty of Electrical Engineering and Communication BUT
Department:Department of Control and Instrumentation FEEC BUT

Learning objectives:
The goal of the course is to acquaint with the fundamentals of fuzzy sets theory and fuzzy logic. Students learn to apply the fuzzy theory for modelling of te uncertainty systems. They acquaint with adaptive techniques in the fuzzy systems.
Description:
Motivation, crisp sets and fuzzy sets. Fuzzy sets operations, t-norms and conorms. Fuzzy relations and operations with them. Projection, cylindrical extension, composition. Approximate reasoning. Linguistic variable. Fuzzy implication. Generalized modus ponens and fuzzy rule "if-then". Inference rules. The evaluation of a set of the fuzzy rules. Fuzzy systems Mamdani and Sugeno. The structure of the system, knowledge and data base. Fuzzification and defuzzification. Fuzzy system as an universal approximator. Adaptive fuzzy systems, neuro fuzzy systems.
Learning outcomes and competencies:
The student has fundamental knowledge and skill in the fuzzy theory. He knows to apply it in the field of the modelling and control of the uncertainty defined systems.
Syllabus of lectures:

• Motivation, crisp sets and fuzzy sets.
• Operation with the fuzzy sets.
• t-norm a conorm.
• Fuzzy relation and operations with them. Projection, cylindrical extension, composition.
• Approximate reasoning. Linguistic variable. Fuzzy implication.
• Generalised "modus ponens", fuzzy rule "if-then". Inference rules.
• Evaluation of the set of fuzzy rules.
• Fuzzy systems Mamdani a Sugeno.
• The structure of the fuzzy system, knowledge and data base.
• Fuzzification and defuzzification.
• Fuzzy system is an universal approximator.
• Neuro-fuzzy systems.
Syllabus - others, projects and individual work of students:
Mamdani or Sugeno type model in one implemented example.
Fundamental literature:

• Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Logic, Springer-Verlag, 1993 ISBN 3-540-56362-8.
Study literature:

• Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Logic, Supported book, Springer-Verlag, 1993, ISBN 80-214-2261-0.
Progress assessment:
One mid-semestr written test.
Exam prerequisites:
Working out of the project.