| Course Type | Elective Course |
|---|
| Course Level | Doctorate Of Science |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- will be familiar with the main concepts of Fuzzy Set Theory such as membership degree, membership function, ?-level set, fuzzy number and with operations on fuzzy sets.
- will be able to represent a fuzzy proposition using membership function, will be familiar with conditional fuzzy propositions and fuzzy inference methods
- will be aware of the use of fuzzy inference methods in the design of intelligent systems, and will be familiar with fuzzy logic applications in robotics, control, etc.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Introduction to Fuzzy Logic and Fuzzy Set Theory. Operations on fuzzy sets. Fuzzy relations and Fuzzy inferences. Approximate reasoning theory. Fuzzy logic controllers and applications of fuzzy systems. Artificial neural networks.Method of sensor learning.Delta learning method. Artificial neural network applications. Fuzzy neural networks and Fuzzy nerves. Hybrid neural networks. Fuzzy rule extraction from numerical data. Applications of hybrid neural networks. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to the Theory of Fuzzy Sets and Fuzzy Logic. |
|
2nd Week |
Operations on fuzzy sets. |
|
3rd Week |
Fuzzy relations and Fuzzy implications. |
|
4th Week |
The theory of approximate reasoning. |
|
5th Week |
Fuzzy logic controllers and Applications of fuzzy systems. |
|
6th Week |
Artificial neural networks. |
|
7th Week |
The perceptron learning rule. |
|
8th Week |
Midterm |
|
9th Week |
The delta learning rule. |
|
10th Week |
Applications of artificial neural networks. |
|
11th Week |
Fuzzy neural networks and Fuzzy neurons. |
|
12th Week |
Hybrid neural nets. |
|
13th Week |
Fuzzy rule extraction from numerical data. |
|
14th Week |
Applications of hybrid neural nets. |
|
| Recommended or Required Reading | 1. R. Fuller (2000), Introduction to Neuro-Fuzzy Systems, Springer-Verlag Berlin Heidelberg.
2. T.J. Ross (2016), Fuzzy Logic with Engineering Applications, 4th Edition, Wiley.
3. K.H. Lee (2005), First Course on Fuzzy Theory and Applications, Springer. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Problem Solving
- Report Preparation
- Presentation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
6 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
11 |
154 |
| Preparation for Final exam |
1 |
69 |
69 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
44 |
44 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 313 |
| Total Work Load / 30 (h) | 10.43 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
