Fuzzy Logic
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Course Objectives
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| This course covers fuzzy logic topics used in engineering fields. |
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Course Content
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| The concept of fuzzy, fuzzy sets, fuzzy membership functions, the features of fuzzy sets, theoretic operations in fuzzy sets, fuzzy relations, uncertainty model fuzziness, fuzzy rule based systems and fuzzy decision making, fuzzy system modeling, fuzzy clustering, neural network approach to fuzzy inference systems, Matlab FIS and ANFIS applications and samples. |
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Course materials
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- 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
- Foundations on Neuro-Fuzzy Systems, D. Nauck, F. Klawonn, R. Kruse, Wiley, Chichester, 1997
- Fuzzy Logic with Engineering Applications by T.J. Ross, McGraw-Hill Book Company, 1995.
- Fuzzy Control, K.M. Passino, S.Yurkovich, Addison-Wesley-Longman, 1998.
- Neural Fuzzy Systems: A Neuro-Fuzzy Synergism., by Lin, (1996) , Prentice Hall.
- Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
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Assessment
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%30 Midterm exam + %30 Projects and Homeworks + %40 Final exam
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Prerequisites
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There is no formal requirement, but it is better if the student knows computer programming.
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| Week |
Subjects |
Lecture Notes |
| 1. |
The concept of fuzzy |
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| 2. |
Fuzzy sets |
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| 3. |
Fuzzy membership functions |
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| 4. |
The features of fuzzy sets |
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| 5. |
Theoretic operations in fuzzy sets |
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| 6. |
Fuzzy relations |
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| 7. |
Uncertainty model fuzziness |
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| 8. |
Midterm exam |
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| 9. |
Fuzzy rule based systems and fuzzy decision making |
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| 10. |
Fuzzy system modeling |
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| 11. |
Fuzzy clustering |
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| 12. |
Neural network approach to fuzzy inference systems |
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| 13. |
Matlab FIS and ANFIS applications |
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| 14. |
Matlab FIS and ANFIS applications |
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| 15. |
Project Presentation |
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Final Exam |
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