Course Name Code Semester T+U Hours Credit ECTS
Soft Computing Methods and Applications ENM 555 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Dr.Öğr.Üyesi ALPER KİRAZ
Course Lecturers
Course Assistants
Course Category Field Proper Education
Course Objective


Using soft computing methods for modeling problems including multivariable and multi-parameter and difficult to model, solving these kind of problems with soft computing methods and interpreting results.

Course Content

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.

# Course Learning Outcomes Teaching Methods Assessment Methods
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,
Week Course Topics Preliminary Preparation
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)
Resources
Course Notes
Course Resources

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.

Order Program Outcomes Level of Contribution
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.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 50
1. Performans Görevi (Uygulama) 50
Total 100
1. Yıl İçinin Başarıya 60
1. Final 40
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
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
Total Workload 150
Total Workload / 25 (Hours) 6
dersAKTSKredisi 6