| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Fuzzy Logıc and Artıfıcıal Neural Network | ISE 431 | 7 | 3 + 0 | 3 | 5 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | Lisans |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Dr.Öğr.Üyesi TUĞRUL TAŞCI |
| Dersi Verenler | |
| Dersin Yardımcıları | |
| 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, Oral Exam, |
| 2 | Understand basic knowledge about neural Networks | Lecture, Question-Answer, Drilland Practice, | Testing, Homework, |
| 3 | Understand using the fuzzy logic and ANN for encountered problems | Lecture, Question-Answer, Drilland Practice, | Testing, Homework, |
| 4 | Comrehend common fuzzy inference methods | Lecture, Question-Answer, Discussion, | Testing, Homework, |
| 5 | Comprehend sample fuzzy logic and ANN tools | Lecture, Question-Answer, Discussion, | Testing, Oral Exam, Homework, |
| 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 | Fuzzy Logic and NN, Sakarya University, Notes |
| 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 2.S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan Publishing Company, Englewood Cliffs, NJ, 1994 3.Nazife Baykal, Timur Beyan, Bulanık Mantık İlke ve Temelleri, Seçkin Yayınları, 2004, Ankara |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | X | ||||||
| 2 | X | ||||||
| 3 | X | ||||||
| 4 | X | ||||||
| 5 | X | ||||||
| 6 | X | ||||||
| 7 | X | ||||||
| 8 | X | ||||||
| 9 | X | ||||||
| 10 | X | ||||||
| 11 | X | ||||||
| 12 | X | ||||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 50 |
| 1. Kısa Sınav | 20 |
| 1. Ödev | 15 |
| 2. Ödev | 15 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 10 |
| 1. Final | 90 |
| 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 | 3 | 3 |
| Quiz | 1 | 3 | 3 |
| Assignment | 2 | 3 | 6 |
| Final examination | 1 | 8 | 8 |
| Toplam İş Yükü | 116 | ||
| Toplam İş Yükü / 25 (Saat) | 4,64 | ||
| Dersin AKTS Kredisi | 5 | ||