| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Soft Computıng Methods and Applıcatıons | ENM 555 | 0 | 3 + 0 | 3 | 6 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | YUKSEK_LISANS |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Doç.Dr. ALPER KİRAZ |
| Dersi Verenler | |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Alanına Uygun Öğretim |
| Dersin Amacı |
|
| Dersin İçeriği | Principal concepts of soft computing, Fuzzy set theory and applications, Principal concepts of Neuro-computing and applications pf artificial neural networks, Evolutionary computing and applications of Genetic Algorithms, Importance of soft computing in fuzzy decision making, applications of soft computing methods on Matlab toolboxes and interpreting results. |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Students know the principal concepts of soft computing | Lecture, Question-Answer, Discussion, | Testing, Oral Exam, Homework, |
| 2 | Students are informed of methods of soft computing | Lecture, Question-Answer, Discussion, Drilland Practice, | Testing, Oral Exam, Homework, |
| 3 | Students define and solve problems using soft computing methods | Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, | Testing, Oral Exam, Homework, |
| 4 | Students are informed of decision making techniques | Lecture, Question-Answer, Discussion, | Testing, Oral Exam, Homework, |
| 5 | Students Define and solve problems using decision making techniques | Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, | Testing, Oral Exam, Homework, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Basic concepts of Soft Computing | |
| 2 | Introduction to Artificial Intelligence | |
| 3 | Introduction to MATLAB | |
| 4 | Basic applications on MATLAB and toolboxes | |
| 5 | Fuzzy set theory and creation of fuzzy models | |
| 6 | Fuzzy logic applications on MATLAB | |
| 7 | Neurocomputing and creation of artificial neural network models | |
| 8 | Applications of artificial neural networks on MATLAB | |
| 9 | Midterm | |
| 10 | Evolutionary computing and creation of genetic algorithm models | |
| 11 | Applications of genetic algorithms on MATLAB | |
| 12 | Based on artificial intelligence decision making and decision support systems | |
| 13 | Fuzzy multi criteria decision making methods (Fuzzy AHP, Fuzzy DEMATEL) | |
| 14 | Fuzzy multi criteria decision making methods (Fuzzy TOPSIS) |
| Kaynaklar | |
|---|---|
| Ders Notu | |
| Ders Kaynakları | Hızıroğlu, A., Kiraz, A., Cebeci, H. İ., Taşkın, H., Selvi, İ. H., Codal, K. S., İpek, M., Şişci, Ş. M. “Esnek Hesaplama: İşletme ve Ekonomide Uygulamaları”, Çeviri Kitap, ISBN: 978-605-4735-80-8, 2017. Kubat, C., MATLAB Yapay Zeka ve Mühendislik Uygulamaları, Pusula Yayıncılık ve İletişim, 2016. Figueira, J., Greco, S., Ehrgott, M., Multi Criteria Decision Analysis State of the Art Surveys, Springer, International Series in Operations Research & Management Science, 2005. Yıldırım, B., F., Önder, E., Çok Kriterli Karar Verme Yöntemleri, Dora Yayıncılık, 2016. |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | The aim of the course is to reach the information in depth and in depth by conducting scientific research in the field of engineering, to evaluate, interpret and apply the information. | X | |||||
| 2 | Ability to complete and apply knowledge by scientific methods using limited or missing data; to integrate information from different disciplines. | ||||||
| 3 | To be able to construct engineering problems, develop methods to solve them and apply innovative methods in solutions. | ||||||
| 4 | Ability to develop new and original ideas and methods; develop innovative solutions in system, part or process designs. | ||||||
| 5 | Ability to design and apply analytical, modeling and experimental research; to analyze and interpret complex situations encountered in this process. | ||||||
| 6 | Identify the information and data needed, reach them and evaluate them at an advanced level. | ||||||
| 7 | Leadership in multi-disciplinary teams, developing solutions to complex situations and taking responsibility. | ||||||
| 8 | To be able to convey the process and results of his / her studies systematically and clearly in written or oral form in national and international environments in or out of that field. | ||||||
| 9 | Interpreting comprehensive information about modern techniques and methods applied in engineering and their limits. | ||||||
| 10 | Awareness about new and developing practices of the profession; to examine and learn them when necessary. | ||||||
| 11 | To understand the social and environmental dimensions of engineering applications and to adapt to the social environment. | ||||||
| 12 | To observe social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. | ||||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 50 |
| 1. Performans Görevi (Uygulama) | 50 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 60 |
| 1. Final | 40 |
| 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 | 2 | 32 |
| Mid-terms | 1 | 20 | 20 |
| Performance Task (Application) | 1 | 30 | 30 |
| Final examination | 1 | 20 | 20 |
| Toplam İş Yükü | 150 | ||
| Toplam İş Yükü / 25 (Saat) | 6 | ||
| Dersin AKTS Kredisi | 6 | ||