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| BIL4006 | Fuzzy Logic | 3+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Yeşim Aysel BAYSAL ASLANHAN | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course aims to provide students with the necessary foundation in the basic principles and applications of fuzzy logic and to demonstrate its applications in various systems. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | learn fuzzy set theory, fuzzy logic, properties of fuzzy sets and fuzzy logic. | 2,3,4,12 | 1,3, | | LO - 2 : | apply fuzzy operators. Fuzzy relation, extension principles. | 2,3,4,12 | 1,3, | | LO - 3 : | apply fuzzy approximate reasoning. Fuzzy rules, fuzzification and defuzzification. | 2,3,4,12 | 1,3, | | LO - 4 : | develope fuzzy logic controllers. and gain the ability to apply fuzzy logic to different systems. | 2,3,4,12 | 1,3, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Fuzzy set theory, fuzzy logic, properties of fuzzy sets and fuzzy logic. Fuzzy operators. Fuzzy relation, extension principles. Fuzzy approximate reasoning. Fuzzy rules, fuzzification and defuzzification. Fuzzy logic controllers. Other applications of fuzzy logic. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | A brief history of fuzzy logic and the concept of fuzziness | | | Week 2 | Fuzzy sets, Membership function | | | Week 3 | Fuzzy sets specifications | | | Week 4 | Basic fuzzy operations | | | Week 5 | Fuzzy relations and association | | | Week 6 | Uncertainty of the fuzzy model: Fuzzy clustering and partitioning | | | Week 7 | Fuzzy rule-based systems and fuzzy decision making, Mamdani fuzzy modeling | | | Week 8 | Sugeno and Tsukamoto fuzzy modeling | | | Week 9 | Mid-term exam | | | Week 10 | Defuzzification methods | | | Week 11 | Fuzzy logic controller structure and design | | | Week 12 | An application example and simulation of the fuzzy logic controller | | | Week 13 | Different fuzzy logic application examples | | | Week 14 | All matters related to Matlab / Simulink and the examples I | | | Week 15 | All matters related to Matlab / Simulink and the examples II | | | Week 16 | End-of-term exam | | | |
| 1 | Altaş, İ.H., Ders sunum notları, Basılmamış, KTÜ | | | |
| 1 | Jang, J.S.R., Sun, C.T. and Mizutani, E., 1996; Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall | | | 2 | Nauck, D., Klawonn, F., Kruse, R., 1997; Foundations of Neuro-Fuzzy Systems, Wiley | | | 3 | Ross, T.J., 1995; Fuzzy Logic with Engineering Applications, McGraw-Hill Book Company | | | 4 | Passino, K.M., Yurkovich, S., 1998; Fuzzy Control, Addison-Wesley-Longman. | | | 5 | Lin, 1996; Neural Fuzzy Systems: A Neuro-Fuzzy Synergism, Prentice Hall. | | | 6 | Klir, G.J. and Folger, T.A., 1988; Fuzzy Sets, Uncertainity, and Information | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Homework/Assignment/Term-paper | 3
5
7
11
13 | | 20 | 20 | | End-of-term exam | 16 | | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Arasınav için hazırlık | 2 | 3 | 6 | | Arasınav | 2 | 1 | 2 | | Ödev | 4 | 5 | 20 | | Dönem sonu sınavı için hazırlık | 2 | 3 | 6 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 106 |
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