Fuzzy logic

Course: Software engineering

Structural unit: Faculty of Computer Science and Cybernetics

Title
Fuzzy logic
Code
ОК.06
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
1 Semester
Number of ECTS credits allocated
3
Learning outcomes
PLO01. Know and systematically apply methods of analysis and modeling of the application area, identifying information needs and collecting source data for software design. PLO08. Conduct analytical research on the parameters of software systems for their validation and verification, as well as analyze the selected methods, tools for automated design and implementation of software. PLO13. Prepare research results in the form of articles in scientific journals and abstracts of reports at scientific and technical conferences PLO14. Explain, analyze, purposefully search for and select the necessary for the solution of professional scientific and applied problems of information and reference and scientific and technical resources and sources of knowledge, taking into account modern advances in science and technology.
Form of study
Distance form
Prerequisites and co-requisites
1. Know the basics of software development using modern programming languages, some chapters of mathematical analysis, algebra, programming and probability theory. 2. Be able to: solve equations and systems of linear algebraic equations. 3. Have skills: elements of integral calculus and probability theory.
Course content
The purpose of the discipline is to master the theoretical issues of fuzzy algorithmization, the main methods of solving problems in fuzzy formulation, the means of building fuzzy models of problems in different subject areas. As a result of studying the discipline the student must: know information about the basic concepts and definitions of fuzzy set theory, principles of designing intelligent software systems with fuzzy models of knowledge representation; be able to design and develop fuzzy models of knowledge representation, choose the most successful membership functions for the implementation of the software project; use development tools.
Recommended or required reading and other learning resources/tools
1. D. Rutkovskaya, M. Pilinsky, L. Rutkovsky. Neural networks, genetic algorithms and fuzzy systems. M .: Telekom, 2006. - 382 p. 2. J. Leski. Systemy neuronowo-rozmyte. Warszawa: Naukowo-Techniczne, 2008. – 690 c. 3. Zadeh L.A. Fuzzy sets as a basis for a theory of possibility // Fuzzy Sets and Systems, 1978, N1, p. 3–28.
Planned learning activities and teaching methods
Lectures, independent work (project), tests, homework, exam.
Assessment methods and criteria
1. Test 1: LO 1.1, LO1.2, LO2.1 - 10 points / 6 points. 2. Test 2: LO 1.2, LO1.3, LO2.1 - 10 points / 6 points. 3. Independent work 1: LO1.2, LO1.3, LO2.1, LO3.1 – 20 points /12 points. 4. Independent work 2: LO1.2, LO1.3, LO2.1, LO3.1 – 20 points /12 points. Final assessment (in the form of an exam): - maximum number of points: 40 points; - learning outcomes which shall be assessed: LO1.1, LO1.2, LO1.3, LO2.1: - form of examination and types of tasks: written work, 4 written assignments.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Oleksandr I. Provotar
Department of Intelligent Software Systems
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Department of Intelligent Software Systems
Faculty of Computer Science and Cybernetics