| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | MAT-23-101 | FUZZY SETS AND APPLICATIONS | Elective | 1 | 1 | 6 |
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| Level of Course Unit |
| Second Cycle |
| Objectives of the Course |
| Fuzzy Set Theory is used to solve complex, complex, ambiguous or nonlinear systems or problems that cannot be easily solved by classical set theory or probability theory. This lesson is studied on the basis of fuzzy set theory and fuzzy logic. In addition, this course also explains fuzzy logic applications such as fuzzy control and fuzzy decision making in many areas. |
| Name of Lecturer(s) |
| Dr.Öğr.Üyesi Gökçe Dilek KÜÇÜK |
| Learning Outcomes |
| 1 | Developes and deepens knowledge in the related program’s area based upon the competency in the undergraduate level; reaches, evaluates, interprets and applies knowledge by doing research. | | 2 | Has enough knowledge in theory and practice at international level. | | 3 | Gain the ability to analyze and design the problematic problem that exists in the direction of a defined target. | | 4 | Gain the ability to perform interdisciplinary and interdisciplinary teamwork. |
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| Mode of Delivery |
| Daytime Class |
| Prerequisites and co-requisities |
| It is need to know the basic facts of mathematics courses in the graduate level. |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| Fuzzy Sets Basic Definitions
Extensions
Fuzzy Measures and Measures of Fuzzy Meaning of Course Materials
Extension principle and applications
Fuzzy Relations and Fuzzy Graphs
Fuzzy Analysis
The Theory of Opportunity, Probability Theory and Fuzzy Set Theory Lecture
Fuzzy Sets and Expert Systems
Fuzzy Control
Fuzzy Data Analysis
Decision Making in Fuzzy Environments
Fuzzy Sets and Expert Systems |
| Weekly Detailed Course Contents |
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| 1 | Fuzzy Sets Basic Definitions | | | | 2 | Extensions | | | | 3 | Fuzzy Measures and Measures of Fuzzy Meaning of Course Materials | | | | 4 | Extension principle and applications | | | | 5 | Fuzzy Relations and Fuzzy Graphs | | | | 6 | Fuzzy Analysis | | | | 7 | The Theory of Opportunity, Probability Theory and Fuzzy Set Theory Lecture | | | | 8 | Mid-Term Exam | | | | 9 | Fuzzy Sets and Expert Systems | | | | 10 | Fuzzy Control | | | | 11 | Fuzzy Data Analysis | | | | 12 | Decision Making in Fuzzy Environments | | | | 13 | Decision Making in Fuzzy Environments | | | | 14 | Fuzzy Sets and Expert Systems | | | | 15 | Final exam | | |
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| Recommended or Required Reading |
| 1. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, by J.S.R. Jang, C.T. Sun, and E. Mizutani, Prentice Hall, 1996
2. Foundations on Neuro-Fuzzy Systems, D. Nauck, F. Klawonn, R. Kruse, Wiley, Chichester, 1997.
3. Fuzzy Logic with Engineering Applications by T.J. Ross, McGraw-Hill Book Company, 1995.
4. Fuzzy Control, K.M. Passino, S.Yurkovich, Addison-Wesley-Longman, 1998.
5. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism., by Lin, (1996) , Prentice Hall.
6. Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
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| Planned Learning Activities and Teaching Methods |
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| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 50 | | End Of Term (or Year) Learning Activities | 50 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
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| Workload Calculation |
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| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 10 | 10 |
| Makeup Examination | 1 | 1 | 1 |
| Attending Lectures | 14 | 3 | 42 |
| Problem Solving | 3 | 10 | 30 |
| Criticising Paper | 3 | 10 | 30 |
| Self Study | 14 | 4 | 56 |
| Individual Study for Mid term Examination | 1 | 10 | 10 |
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| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | | LO2 | 5 | 5 | 5 | 5 | 5 | 5 | | 5 | 5 | 5 | 5 | | LO3 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | | LO4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
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| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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