Infopaket

Fuzzy sets and fuzzy logic

ВБ 1.10

Вибіркова дисципліна для ОП

First

2021/2022

8 Semester

4

Use methods of computational intelligence, machine learning, fuzzy data processing, genetic and evolutionary programming to solve problems of recognition, forecasting, classification, identification of control objects, etc; design, develop, and analyze algorithms for solving computational and logical problems, evaluate the efficiency and complexity of algorithms based on the application of formal algorithm models and computable functions; apply "soft computing" technologies and expert assessment to solve practical problems in various subject areas under deterministic conditions, conditions of uncertainty, risk, and conflict situations.

Full-time form

Know probability theory, optimization methods, and decision-making. Be able to use this knowledge in solving practical problems. Possess skills in working with basic software.

Within the framework of the discipline "Fuzzy Sets and Fuzzy Logic," fuzzy sets, fuzzy relations, their representation methods, and operations on them, classification of membership functions, methods of their construction, fuzzy quantities and operations on them, fuzzy and linguistic variables, fuzzy statements, and ways of their definition, fuzzy inference, fuzzy inference systems, and their applications are considered. As a result of studying the discipline, the student should know the fundamental sections of fuzzy set theory necessary for conducting scientific research in the field of mathematical support of information technologies, be able to model different types of uncertainty, possess the mathematical apparatus of fuzzy set theory, fuzzy inference, and soft computing technologies necessary for the development of decision support information systems oriented towards processing information under conditions of uncertainty.

1. Prokhorova O. M., N. V. Kalchuk. Models and Methods of Fuzzy Logic: Educational Manual. National Aerospace University named after N. Ye. Zhukovsky "KhAI", 2021. 166 p. 2. Kondratenko Y. P., Sidenko Ye.V. Fuzzy Sets and Fuzzy Logic. Methodical Recommendations and Instructions for Performing Laboratory Work by Students of Specialty 122 "Computer Science". - Mykolaiv: Petro Mohyla ChNU, 2019. - 36 p. 3. P. Kravets, R. Kirkalo. Decision-making Systems with Fuzzy Logic. Lviv Polytechnic National University. 2009. Pp. 115-123. 4. Razzhivin, O. V., Subotin O. V. Synthesis of Fuzzy Controllers in Automatic Control Systems: Educational Manual. Kramatorsk: CTRI "Printing House", 2017. 212 p. 5. Trius Yu. V., Galasun K. I. Fuzzy Models and Methods in Decision-Making Systems: Educational Manual. Cherkasy, ChDTU, 2013. 112 p.

Lectures, laboratory classes, individual work.

The level of achievement of all planned learning outcomes is determined by the results of written tests and the completion of independent work. The maximum number of points a student can earn for work during the semester does not exceed 60 points on a 100-point scale. The final assessment is conducted in the form of an exam. The maximum number of points a student can obtain is 40 points on a 100-point scale. If a student receives less than 24 points during the exam, they are given an "unsatisfactory" grade, and the earned points are not counted. A student is not allowed to take the exam if they have scored less than 36 points during the semester (less than 60% of the maximum possible number of points a student can earn for their work during the semester).

Ukrainian