| 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;
- Explain the advantage and disadvantage of Intelligent Systems.
- Gain an ability to know how choosing Intelligent Systems for real applications.
- Learn the application of Intelligent Systems in real problems.
- Gain an ability to choose the right method for defined problem.
- Gain an ability to develop programs aimed for Intelligent Systems employment using MATLAB or similar programing tools.
|
| Mode of Delivery | Face to face |
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| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Introduction to intelligent systems and soft computing, Fuzzy set theory, Fuzzy inference methods, Fuzzy clustering, Fuzzy logic control and application to nonlinear systems. Introduction to neural Networks, Adaptive Neuro-Fuzzy Inference system (ANFIS), Neuro-Fuzzy Classification (Nefclass), Introduction to intelligent optimization techniques and engineering applications. Course project |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Intelligent Systems |
|
2nd Week |
Fuzzy clustering theory |
|
3rd Week |
Rule Based Control |
|
4th Week |
Fuzzy logic control and its applications (P,PI,PD,PID) |
|
5th Week |
Fuzzy logic control and its applications (P,PI,PD,PID) |
|
6th Week |
Intelligent classification methods (NN,SVM) |
|
7th Week |
Intelligent classification methods (NN,SVM) |
|
8th Week |
Midterm Exam |
|
9th Week |
Intelligent clustering methods (NN, Fuzzy Logic) |
|
10th Week |
Neuro-Fuzzy inference systems (Anfis) |
|
11th Week |
Neuro-Fuzzy classification (Nefclass) |
|
12th Week |
Evolutionary optimization algorithms (GA, PSO) |
|
13th Week |
Evolutionary optimization algorithms (GA, PSO) |
|
14th Week |
Evolutionary optimization algorithm based fuzzy logic control |
|
| Recommended or Required Reading | 1. Artificial Intelligence: A Guide to Intelligent Systems", Negnevitsky, second edition
2. Lecture notes |
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| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
15 |
| Project |
1 |
25 |
| Total(%) |
|
70 |
| Contribution of In-term Studies to Overall Grade(%) |
|
70 |
| Contribution of Final Examination to Overall Grade(%) |
|
30 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
3 |
3 |
| Preparation for Quiz |
0 |
0 |
0 |
| Individual or group work |
14 |
8 |
112 |
| Preparation for Final exam |
1 |
40 |
40 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
30 |
30 |
| Laboratory (including preparation) |
0 |
0 |
0 |
| Final exam |
1 |
3 |
3 |
| Homework |
2 |
35 |
70 |
| Total Work Load | 300 |
| Total Work Load / 30 (h) | 10 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
