Course Name Code Semester T+U Hours Credit ECTS
Intelligent Modeling, Optimization and Control ENM 602 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Doctorate Degree
Course Type Optional
Course Coordinator Dr.Öğr.Üyesi MEHMET RIZA ADALI
Course Lecturers Dr.Öğr.Üyesi MEHMET RIZA ADALI,
Course Assistants Res. Assist. Ü. Atakan KAHRAMAN
Course Category
Course Objective Giving a general skill and competency about intelligent techniques and ability of using these methods to model, optimize, and control processes to graduate students
Course Content Classical modeling, optimization and control, reasoning and inference under uncertainty, Fuzzy sets, operations, relationships, numbers, variables, and fuzzy logic, rule-based computations, Fuzzy-neural and evolutionary computations, Fuzzy modeling, problem solving with fuzzy sets, Methodology, Fuzzy optimization and control
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Learning the basic concepts of fuzzy sets and models
2 Achieving the ability of modeling processes by using intelligent techniques
3 Achieving the ability of setting up and solving a variety of systems by using fuzzy logic, neural networks, genetic algorithms, and hybrid methods
Week Course Topics Preliminary Preparation
1 Classical Modeling, Optimization and Control
2 Reasoning and Inference Under Uncertainity
3 Fuzzy Sets and Operations
4 Fuzzy Relations and Numbers
5 Fuzzy Variables and Fuzzy Logic
6 Rule-Based Computations
7 Fuzzy Neurocomputations
8 Fuzzy Evolutionary Computations
9 Fuzzy Modeling I
10 Fuzzy Modeling II
11 Problem Solving with Fuzzy Sets: Introduction
12 Methodology
13 Fuzzy Optimization and Control
14 Case Studies (Traffic Intersection Control, Chemical Process Control, Manufacturing Control)
Course Notes [1] Prof. Dr. Harun Taşkın, Intelligent Modelling Optimizatıon And Control Lecture Notes
Course Resources [2] ZADEH L.A., Fuzzy Sets, Information and Control, 8, (1965), 338-353
[3] LI, H., CHEN, C. L. P., HUANG, H.P., Fuzzy Neural Intelligent Systems: Mathematical Foundations and the Application in Engineering, CRC Pres, New York, 2000
[4] ROSS, T. J., Fuzzy Logic with Engineering Applications, Mc Graw Hill, New York, 1995
[5] WANG, L., X., A Course in Fuzzy Systems and Control, Prentice Hall, 1997
[6] TERANO T., ASAI, K., SUGENO, M., ASCHMANN, C., G., Fuzzy Systems Theory and Its Applications, Academic Press Inc., 1991
[7] PEDRYCZ, W., GOMIDE, F., An Introduction to Fuzzy Sets: Analysis and Design, A Bradford Book, 1998
[8] BAYKAL, N., BEYAN, T., Bulanık Mantık İlke ve Temelleri, Bıçaklar Kitabevi, 2004
[9] YEN, J., LANGARI, R., Fuzzy Logic: Intelligence, Control, and Information, Prentice Hall, 1998
[10] JAMSHIDI, M., Large-Scale Systems: Modeling, Control, and Fuzzy Logic, Prentice Hall, 1997
[11] JAMSHIDI, M., ZADEH, L.A., TITLI, A., Applications of Fuzzy Logic: Towards High Machine Intelligence Quotient Systems, Prentice Hall, 1997
[12] KARRAY, F.O., SILVA, C.W., Soft Computing and Intelligent Systems Design: Theory, Tools and Applications, Addison-Wesley, 2004
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.
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 20
1. Kısa Sınav 10
1. Ödev 20
2. Ödev 20
3. Ödev 30
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 3 48
Mid-terms 1 7 7
Quiz 1 5 5
Assignment 5 5 25
Performance Task (Seminar) 1 5 5
Final examination 1 10 10
Total Workload 148
Total Workload / 25 (Hours) 5.92
dersAKTSKredisi 6