Course detail

Mathematical Foundations of Fuzzy Logic

FIT-IMFAcad. year: 2023/2024

At the beginning of the semester, students choose from the supplied topics. On the weekly seminars, they present the topics and discuss them. The final seminar is for assessment of students' performance.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. RNDr. Dana Hliněná, Ph.D.

Department

Department of Mathematics (UMAT)

Entry knowledge

Knowledge of "IDA - Discrete Mathematics" and "IMA - Mathematical Analysis" courses.

Rules for evaluation and completion of the course

  • Active participation in the exercises (group problem solving, evaluation of the ten exercises): 30 points.
  • Projects: group  presentation, 70 points.

  • Active participation in the exercises (group problem solving, evaluation of the ten exercises): 30 points.
  • Projects: group  presentation, 70 points.

Aims

To extend an area of mathematical knowledge with an emphasis on solution searchings and mathematical problems proofs.
Successful students will gain deep knowledge of the selected area of mathematics (depending on the seminar group), and the ability to present the studied area and solve problems within it. The ability to understand advanced mathematical texts, the ability to design nontrivial mathematical proofs.

Study aids

Not applicable.

Prerequisites and corequisites

Basic literature

Not applicable.

Recommended reading

Carlsson, Ch., Fullér, R., Fuzzy reasoning in decision making and optimization, Studies in Fuzziness and Soft Computing, Vol. 82, 2002.

Classification of course in study plans

  • Programme BIT Bachelor's, 2. year of study, winter semester, elective

  • Programme IT-BC-3 Bachelor's

    branch BIT , 2. year of study, winter semester, elective

  • Programme BIT Bachelor's, 2. year of study, winter semester, elective

Type of course unit

 

Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer

doc. RNDr. Dana Hliněná, Ph.D.

Syllabus

  1. From classical logic to fuzzy logic
  2. Modelling of vague concepts via fuzzy sets
  3. Basic operations on fuzzy sets
  4. Principle of extensionality
  5. Triangular norms, basic notions, algebraic properties
  6. Triangular norms, constructions, generators
  7. Triangular conorms, basic notions and properties
  8. Negation in fuzzy logic
  9. Implications in fuzzy logic
  10. Aggregation operators, basic properties
  11. Aggregation operators, applications
  12. Fuzzy relations
  13. Fuzzy preference structures

Project

26 hours, compulsory

Teacher / Lecturer

doc. RNDr. Dana Hliněná, Ph.D.

Syllabus

  1. Triangular norms, class of třída archimedean t-norms
  2. Triangular norms, construction of continuous t-norms
  3. Triangular norms, construction of non-continuous t-norms
  4. Triangular conorms
  5. Fuzzy negations and their properties
  6. Implications in fuzzy logic
  7. Aggregation operators, averaging operators
  8. Aggregation operators, applications
  9. Fuzzy relations, similarity, fuzzy equality
  10. Fuzzy preference structures