Faculty of Information Technology, BUT

Course details

Fuzzy Systems for Control and Modelling

FSY Acad. year 2011/2012 Summer semester 5 credits

Current academic year

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.

Guarantor

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

55 exam, 15 half-term test, 30 projects

Department

Lecturer

Subject specific learning outcomes and competences

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.

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.

Study literature

  • Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Logic, Supported book, Springer-Verlag, 1993, ISBN 80-214-2261-0.

Fundamental literature

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

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.
  • Adaptive fuzzy systems.
  • Neuro-fuzzy systems.

Syllabus - others, projects and individual work of students

Mamdani or Sugeno type model in one implemented example.

Progress assessment

One mid-semestr written test.

Exam prerequisites

Working out of the project.

Course inclusion in study plans

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