| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Fuzzy Logıc and Artıfıcıal Neural Network | EBT 542 | 0 | 3 + 0 | 3 | 6 |
| Ön Koşul Dersleri | N/a |
| Önerilen Seçmeli Dersler | N/A |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | YUKSEK_LISANS |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Dr.Öğr.Üyesi MUHAMMED FATİH ADAK |
| Dersi Verenler | Dr.Öğr.Üyesi MUHAMMED FATİH ADAK, |
| Dersin Yardımcıları | Yrd. Doç. Dr. Seçkin Arı |
| Dersin Kategorisi | Diğer |
| Dersin Amacı | The fuzzy logic has the capability of solving complex non-linear system using human intelligence and reasoning model. Neural Networks are used for modelling of the brain functions to solve complex non-linear system. This course presents basic knowledge about fuzzy logic, neural Networks and applications |
| Dersin İçeriği | Fuzzy sets. Membership functions. Fuzzy operations. T-norm, N- norm operator. Fuzzy Rules Fuzzification, defuzzification. Fuzzy inferrence. Mamdani fuzzy inference. Mamdani fuzzy inference applications. Sugenoi fuzzy inference and applications. Matlab fuzzy applications. The structure of the brain. Artificial Neuron. Perceptron. Multilayer neural networks. Learning. Back propagation algorithm. Momentum coefficient. Matlab neural network applications |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Understand basic knowledge about fuzzy logic | Lecture, Question-Answer, | Testing, Project / Design, |
| 2 | Understand basic knowledge about neural Networks | Lecture, Question-Answer, | Testing, Project / Design, Performance Task, |
| 3 | Understand using the fuzzy logic and ANN for encountered problems | Lecture, Question-Answer, Brain Storming, | Testing, Oral Exam, Homework, |
| 4 | Comrehend common fuzzy inference methods | Lecture, Problem Solving, | Testing, Oral Exam, Homework, |
| 5 | Comprehend sample fuzzy logic and ANN tools | Lecture, Question-Answer, Self Study, | Oral Exam, Homework, Performance Task, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Fuzzy sets. Membership functions | |
| 2 | Fuzzy operations. T-norm, N- norm operator | |
| 3 | Fuzzy Rules Fuzzification, defuzzification. Fuzzy inferrence | |
| 4 | Mamdani fuzzy inference | |
| 5 | Mamdani fuzzy inference applications | |
| 6 | Sugenoi fuzzy inference and applications | |
| 7 | Matlab fuzzy applications | |
| 8 | The structure of the brain. Artificial Neuron | |
| 9 | Perceptron | |
| 10 | Multilayer neural networks | |
| 11 | Learning | |
| 12 | Back propagation algorithm | |
| 13 | Momentum coefficient | |
| 14 | Matlab neural network applications |
| Kaynaklar | |
|---|---|
| Ders Notu | Lecturer notes for the course will be post on course web page |
| Ders Kaynakları | 1.J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro Fuzzy and Soft Computing, Prentice Hall, Upper Sllade River, NJ 07458, 1997 |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | ability to access wide and deep information with scientific researches in the field of Engineering, evaluate, interpret and implement the knowledge gained in his/her field of study | X | |||||
| 2 | ability to complete and implement limited or incomplete data by using the scientific methods. | X | |||||
| 3 | ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them | X | |||||
| 4 | ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process | X | |||||
| 5 | gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering | X | |||||
| 6 | ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process | X | |||||
| 7 | gain high level ability to define the required information and data | X | |||||
| 8 | ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations | X | |||||
| 9 | systematic and clear verbal or written transfer of the process and results of studies at national and international environments | X | |||||
| 10 | aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement | X | |||||
| 11 | aware of new and developing application of profession and ability to analyze and study on those applications | X | |||||
| 12 | ability to interpret engineering applications social and environmental dimensions and its compliance with the social environment | ||||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 30 |
| 1. Ödev | 35 |
| 2. Ödev | 35 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 40 |
| 1. Final | 60 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 1 | 25 | 25 |
| Final examination | 1 | 20 | 20 |
| Toplam İş Yükü | 141 | ||
| Toplam İş Yükü / 25 (Saat) | 5,64 | ||
| Dersin AKTS Kredisi | 6 | ||