Neural Network Applications
Electrical Engineering
Faculty of Engineering
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Neural Network Applications | MEL322 | Elective | Bachelor's degree | 3 | Spring | 4 |
Name of Lecturer(s)
Associate Prof. Dr. Mehmet Zeki BİLGİN
Assistant Prof. Dr. Esra KANDEMİR BEŞER
Learning Outcomes of the Course Unit
1) Identifying Artificial Neural Network (ANN) Structures
2) Configure the desired ANN Structures
3) Programming the Feed Forward ANN
4) Apply the Back Propagation Algorithm to Feed-Forward Network
5) Applying the ANN to system modeling
6) Applying the ANN to system control
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ||
| Learning Outcomes | |||||||||||
| 1 | Low | Low | No relation | No relation | Middle | No relation | Middle | High | No relation | Low | |
| 2 | Low | Low | No relation | No relation | Low | No relation | Middle | High | No relation | Low | |
| 3 | Low | Low | No relation | No relation | Low | No relation | Middle | High | No relation | Low | |
| 4 | Low | Low | No relation | No relation | Low | No relation | Middle | High | No relation | Middle | |
| 5 | Low | Low | No relation | No relation | Low | No relation | Middle | High | Low | High | |
| 6 | Low | Low | No relation | No relation | High | No relation | High | High | Middle | High | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Introduction to Computer Programing
Course Contents
Introduction, Fundamentals of Artificial Neural Networks (ANN), Neuron Structure and Activation Functions, Perception, Adaline and Medaline Networks, Feed-forward and Multilayered Feed-forward Neural Networks, Back Propagation Algorithms, Self- Organizing Systems, Radial Basis Function, Dynamic Systems and Recurrent Neural Networks, ANNs in System Identification, Neural networks for Control, Applications: Modeling, Observing and Prediction of Dynamic Systems. Application of Neural Network to Electrical Systems. Introduction to Neuro-Fuzzy Systems.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
70% |
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Contribution of Final Examination to Course Grade |
30% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required