| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Fuzzy Logic | MUH415 | Elective | Bachelor's degree | 4 | Fall | 5 |
Name of Lecturer(s)
Prof. Dr. Yaşar BECERİKLİ
Prof. Dr. Mehmet Melih İNAL
Prof. Dr. Haluk KONAK
Prof. Dr. Mehmet YILDIRIM
Associate Prof. Dr. Alev MUTLU
Associate Prof. Dr. Taner ÜSTÜNTAŞ
Learning Outcomes of the Course Unit
1) Comprehend concept of fuzzy sets
2) Implement and learn fuzzy logic principles
3) Comprehend the fuzzy rule generation and rule reduction logic
4) Implement and apply the fuzzy modelling
5) Introduce the advanced topics in fuzzy logic
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | Low | Low | Low | Low | Middle | Middle | No relation | Low | Low | Low | No relation | |
| 2 | Middle | Low | Low | Low | Low | Low | Low | Low | Low | Low | Low | |
| 3 | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 4 | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 5 | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Artificial Intelligence
Course Contents
This lesson covers;crisp sets. introduction to fuzzy logic, fuzzy sets, foundation of fuzzy logic. fuzzification, inference and defauzzification processes, fuzzy numbers, fuzzy rules. fuzzy control, fuzzy identification, fuzzy optimization,training of fuzzy systems and some practical applications.
Weekly Schedule
1) Basic Concept and definition , content, introduction to fuzzy logic2) Introduction to fuzzy logic (FL) What is FL? General features of FL
3) Crisp sets Fuzzy logic sets
4) Basics of FL Fuzzy algebra Fuzzy Association. s-norm Fuzzy intersection: T norm Membership functions
5) Fuzzy systems and inference engine numerical and linguistic variables Fuzzy if-then rules Fuzzy rules Fuzzy inference engine
6) Fuzzification Defuzzification; central avarage method Center of gravity method
7) Classical function approximation Fuzzy universal approximator Approximation properties of Fuzzy systems Fuzzy precision
8) Midterm examination/Assessment
9) MID TERM
10) Fuzzy systems design from the input-output data Fuzzy systems design from the table-lookup
11) Fuzzy system applications general applications Systems identification problem
12) Adaptive fuzzy logic systems
13) Fuzzy classification
14) Control problem Signal processing applications
15) FL applications with MATLAB
16) Final examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Simulation
4) Case Study
5) Problem Solving
6) Project Based Learning
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
50% |
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Contribution of Final Examination to Course Grade |
50% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required