Course Name | Artificial Neural Networks for Signal Processing and Control |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
---|---|---|---|---|---|
EEE 511 | Fall/Spring | 3 | 0 | 3 | 7.5 |
Prerequisites | None | |||||
Course Language | English | |||||
Course Type | Elective | |||||
Course Level | Second Cycle | |||||
Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | ||||||
Course Coordinator | ||||||
Course Lecturer(s) | ||||||
Assistant(s) | - |
Course Objectives | The course aims the students: i) to know basic artificial neural networks models and learning algorithms and ii) to use artificial neural networks models and associated learning algorithms for signal processing and control applications. |
Learning Outcomes | The students who succeeded in this course;
|
Course Description | Artificial neural networks architectures and learning algorithms. Multi layer perceptron, radial basis function networks and support vector machines. Regression / function approximation, classification and clustering. Artificial neural networks for signal processing, filtering and pattern recognition. Artificial neural networks for system identification and control. |
Related Sustainable Development Goals | |
| Core Courses | |
Major Area Courses | ||
Supportive Courses | ||
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
1 | Biological motivation. Historical remarks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
2 | Taxonomy of artificial neural networks and learning algorithms. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
3 | Linear adaptive element, least mean square algorithm and convergence analysis. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
4 | Discrete perceptron, perceptron learning rule and convergence analysis | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
5 | Multi-layer perceptron, back propagation algorithm and its variants with their convergence analyses, overfitting. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
6 | Radial basis function networks, design by input and input-output clustering | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
7 | Support vector machines, Mercer theorem, kernel representation, Lagrange multipliers | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
8 | Generalization,Vapnik-Chervonenkis dimension. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
9 | 1. Midterm | |
10 | Pattern recognition, feature extraction, dimension and data reduction by artificial neural networks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
11 | 1-Dimensional biomedical signal processing by artificial neural networks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
12 | Biomedical image processing by artificial neural networks | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
13 | 2. Midterm | |
14 | Systems identification by artificial neural networks. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
15 | Artificial neural networks based controller design. | Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes., |
16 | Review of the Semester |
Course Notes/Textbooks | The textbook referenced above and lecture notes |
Suggested Readings/Materials | Related Books and Research Papers |
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | 6 | 60 |
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | ||
Project | 2 | 40 |
Seminar / Workshop | ||
Oral Exam | ||
Midterm | ||
Final Exam | ||
Total |
Weighting of Semester Activities on the Final Grade | 8 | 100 |
Weighting of End-of-Semester Activities on the Final Grade | ||
Total |
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | 2 | |
Study Hours Out of Class | 15 | 4 | 60 |
Field Work | |||
Quizzes / Studio Critiques | |||
Portfolio | |||
Homework / Assignments | |||
Presentation / Jury | |||
Project | 2 | 42 | |
Seminar / Workshop | |||
Oral Exam | |||
Midterms | |||
Final Exams | |||
Total | 224 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems. | |||||
2 | To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. | |||||
3 | To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. | |||||
4 | To be able to conduct basic research and write and publish articles in related conferences and journals. | |||||
5 | To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. | |||||
6 | To be able to manage decision-making processes in industrial systems. | |||||
7 | To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary. | |||||
8 | To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering. | |||||
9 | To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria. | |||||
10 | To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest