Electrical and Electronics Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05181818 | Intelligent Control Systems | 4 | Spring | 8 | 3+0+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Asst. Prof. Hüseyin Oktay Altun |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Course Content General methods of forming Neural Network in Intelligent Control Systems, Intelligent Control Systems and Automation, Backward propagation algorithm and Fast-backward propagation algorithm, Radial Based Function Networks in Intelligent Control Systems, Self-feedback Neural Networks in Intelligent Control System applications; Hopfield Network, Self-organizing systems in Intelligent Control System applications, Information-theory models in Intelligent Control Systems, Modular network applications, Kohonen Network in Intelligent Control System applications, General applications in Machine Control Systems, General Applications in Robot Control Systems, Control System Reliability general applications.
Objectives of the Course
The aim of this course is to try to overcome the modeling and solution difficulties in nonlinear control structures by using intelligent based control systems. For this, it will be ensured that ANN model based systems and applications are used.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Artificial Neural Networks: theory and basic principles |
| 2 | General methods in forming Neural Network in Intelligent Control Systems |
| 3 | Back-propagation algorithm in intelligent control systems |
| 4 | Fast-back-propagation algorithm in intelligent control systems |
| 5 | Radial Based Function Networks in Intelligent Control Systems |
| 6 | Hopfield Network in Intelligent Control Systems |
| 7 | Self-organizing systems in Intelligent Control Systems |
| 8 | Self-organizing systems in Intelligent Control Systems |
| 9 | Knowledge-theory models in Intelligent Control Systems |
| 10 | Knowledge-theory models in Intelligent Control Systems |
| 11 | Machine Control Systems |
| 12 | Robotics Control Systems |
| 13 | Robotics Control Systems |
| 14 | Robotics Control Systems |
Textbook or Material
| Resources | Neural Networks and Learning Machines, 3rd Edition, S. Haykin, Pearson Education, 2009. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | - | - |
| Final Exam | - | - |
| Total | 0 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 0 | 0 | 0 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 0 | ||
| Total Work Load / 30 | 0 | ||
| Course ECTS Credits: | 0 | ||