Faculty of Information Technology, BUT

Course details

Mathematical Foundations of Fuzzy Logic

IMF Acad. year 2019/2020 Winter semester 5 credits

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.

Guarantor

Deputy Guarantor

Havlena Vojtěch, Ing. (DITS FIT BUT)

Language of instruction

Czech

Completion

Classified Credit

Time span

26 hrs exercises, 26 hrs projects

Assessment points

30 exercises, 70 projects

Department

Instructor

Subject specific learning outcomes and competences

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.

Generic learning outcomes and competences

The ability to understand advanced mathematical texts, the ability to design nontrivial mathematical proofs.

Learning objectives

To extend an area of mathematical knowledge with an emphasis on solution searchings and mathematical problems proofs.

Why is the course taught

Classical logic only describes well the black and white world. Its consistent use in practical situations can lead to problems. This can be solved with multi-valued, e.g. fuzzy, logic which is the intuitive basis of any conjecture associated with vague terms. The modelling of fuzzy logic connectives is related to the study of the real variable functions. The mathematical apparatus required for the modelling of fuzzy logic connectives is the content of this course.

Prerequisites

Prerequisite kwnowledge and skills

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

Study literature

  • Alsina, C., Frank, M.J., Schweizer, B., Assocative functions: Triangular Norms and Copulas, World Scientific Publishing Company, 2006.
  • Kolesárová, A., Kováčová, M., Fuzzy množiny a ich aplikácie, STU v Bratislave, 2004. (in Czech).
  • Trillas, E., Eciolaza, L, Fuzzy logic-An introductory course for engineering students, Studies in Fuzziness and Soft Computing, 2015.

Fundamental literature

  • Alsina, C., Frank, M.J., Schweizer, B., Assocative functions: Triangular Norms and Copulas, World Scientific Publishing Company, 2006.
  • Baczynski, M., Jayaram, B., Fuzzy implications, Studies in Fuzziness and Soft Computing, Vol. 231, 2008.
  • Carlsson, Ch., Fullér, R., Fuzzy reasoning in decision making and optimization, Studies in Fuzziness and Soft Computing, Vol. 82, 2002.
  • Kolesárová, A., Kováčová, M., Fuzzy množiny a ich aplikácie, STU v Bratislave, 2004. (in Czech).
  • Trillas, E., Eciolaza, L, Fuzzy logic-An introductory course for engineering students, Studies in Fuzziness and Soft Computing, 2015.

Syllabus of numerical exercises

  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

Syllabus - others, projects and individual work of students

  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

Progress assessment

Active participation in the exercises: 30 points.
Projects: 70 points.

Controlled instruction

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

Exam prerequisites

Students have to get at least 50 points during the semester.

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Thucomp.lablectures A112 08:0009:50 2BIA 2BIB 3BIT xx Hliněná , BIB

Course inclusion in study plans

  • Programme BIT, 2nd year of study, Elective
  • Programme IT-BC-3, field BIT, any year of study, Elective
  • Programme IT-BC-3, field BIT, 2nd year of study, Elective
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