Introduction To Fuzzy Logic
Mechatronics Engineering
Faculty of Engineering
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Introduction To Fuzzy Logic | MKT421 | Elective | Bachelor's degree | 4 | Fall | 4 |
Name of Lecturer(s)
Prof. Dr. Hüseyin Metin ERTUNÇ
Learning Outcomes of the Course Unit
1) Explaining the artificial intelligence techniques conceptually.
2) Explaining the difference between the classical sets and fuzzy sets.
3) Constructing fuzzy membership functions for fuzzy problem solving.
4) Constructing rule tables of fuzzification methods.
5) Explaining fuzzy inference techniques and defuzzification methods.
6) Designing a fuzzy logic controller with computer programs.
7) Applying simulation of a dynamical system control using fuzzy logic controller on the computer.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | No relation | No relation | Low | Low | Low | Low | Low | Low | Low | Middle | Middle | |
| 2 | No relation | No relation | Low | Low | No relation | Low | Low | No relation | No relation | Low | No relation | |
| 3 | Low | No relation | Low | No relation | No relation | Low | No relation | No relation | No relation | No relation | Low | |
| 4 | Low | Low | Middle | Low | Low | Middle | No relation | Low | No relation | No relation | Middle | |
| 5 | Low | Low | High | Low | Low | Middle | Low | No relation | No relation | Low | Low | |
| 6 | Low | Middle | Middle | Low | Low | High | No relation | No relation | Low | Low | Low | |
| 7 | Low | Low | High | Middle | Middle | High | No relation | No relation | No relation | Low | Low | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
This course covers; classical sets and fuzzy set theorem, fuzzy logic principals, the basic structure of fuzzy logic controllers, system variables and fuzzy parameters, fuzzification methods, the construction of rule tables, fuzzy inference and defuzzification techniques, the design of fuzzy logic controllers, case studies and applications using fuzzy logic controllers.
Weekly Schedule
1) Introduction to artificial intelligence techniques.2) Fuzziness concept, emphasizing of the difference between the classical logic and fuzzy logic.
3) Fuzzy sets and fuzzy membership functions, the diffrence between classical and fuzzy sets.
4) The features of fuzzy sets
5) Fundamental fuzzy operations: union, intersection, complement, negation.
6) Fuzzy relations.
7) Fuzzy logic rules.
8) Midterm examination/Assessment
9) Fuzzy rule based systems and fuzzy inference
10) Mamdani, Sugeno and TSK models
11) Defuzzification methods
12) The design and simulation of fuzzy logic controllers.
13) Fuzzy modeling at Matlab.
14) The solutions of fuzzy logic problems at Matlab/Simulink.
15) The introduction to Adaptive Neuro-fuzzy inferenc systems (ANFIS))
16) Final examination
Recommended or Required Reading
1- Fuzzy Logic with Engineering Applications, Timothy Ross, Wiley
2- Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, J.S.R. Jang, C.T. Sun, E. Mizutani, Prentice Hall, 1996
3- Fuzzy Logic Toolbox For Use With Matlab, Users Guide, Mathworks
4- Bulanık Mantık İlke ve Temelleri, Nazife Baykal, Bıçaklar Kitabevi, 2004
5- Bulanık Mantık Denetleyiciler, Çetin Elmas, Seçkin Yayıncılık, 2003
Planned Learning Activities and Teaching Methods
1) Lecture
2) Simulation
3) Lab / Workshop
4) Problem Solving
5) Project Based Learning
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
60% |
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Contribution of Final Examination to Course Grade |
40% |
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Total | 100% | ||||||||||||||
Language of Instruction
English
Work Placement(s)
Not Required