| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits | Last Updated Date |
| 1 | EEE651 | SYSTEMS IDENTIFICATION | 3+0+0 | 3 | 6 | 18.05.2026 |
|
Language of Instruction
|
English
|
|
Level of Course Unit
|
Master's Degree
|
|
Department / Program
|
ELECTRICAL AND ELECTRONICS ENGINEERING
|
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Type of Program
|
Formal Education
|
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Type of Course Unit
|
Elective
|
|
Course Delivery Method
|
Face To Face
|
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Objectives of the Course
|
The aim of the course is to provide students with the basic information and skills of system identification.
|
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Course Content
|
Introduction to the course
What is system identification?
Systems, signals, and models
Simulation and prediction
LTI systems and models for identification
Identifiability concept
Nonlinear systems, an introduction
System identification methods, Parameter estimation
LS method
RLS method
Nonparametric methods
Concluding remarks
|
|
Course Methods and Techniques
|
Lectures, HW's.
|
|
Prerequisites and co-requisities
|
None
|
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Course Coordinator
|
Prof.Dr. Tolgay Kara
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Name of Lecturers
|
Associate Prof.Dr. TOLGAY KARA
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Assistants
|
None
|
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Work Placement(s)
|
No
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Recommended or Required Reading
|
Resources
|
Lennart Ljung, System Identification: Theory for the User Prentice Hall information and system sciences series, ISSN 0891-4559
|
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Course Notes
|
Provided during lectures.
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Course Category
|
Mathematics and Basic Sciences
|
%30
|
|
|
Engineering
|
%50
|
|
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Engineering Design
|
%20
|
|
|
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
|
In-Term Studies
|
|
Mid-terms
|
2
|
%
60
|
|
Final examination
|
1
|
%
40
|
|
Total
|
3
|
%
100
|
ECTS Allocated Based on Student Workload
|
Activities
|
Total Work Load
|
|
Weekly lecture hours
|
14
|
3
|
42
|
|
Weekly practical lecture hours
|
14
|
6
|
84
|
|
Reading Activities
|
4
|
5
|
20
|
|
Material design, application
|
2
|
10
|
20
|
|
Midterm and midterm exam preparation
|
1
|
8
|
8
|
|
Total Work Load
| |
|
Number of ECTS Credits 6
174
|
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
|
1
| 1) Be able to develop models for system identification. |
|
2
| 2) Understand LTI system descriptions |
|
3
| 4) Be able to apply parameter estimation method in system identification. |
|
4
| 5) Learn methods of LS and RLS |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Introduction to the course
|
|
|
| 2 |
What is system identification?
|
|
|
| 3 |
Systems, signals, and models
|
|
|
| 4 |
Simulation and prediction
|
|
|
| 5 |
LTI systems and models for identification
|
|
|
| 6 |
LTI systems and models for identification
|
|
|
| 7 |
Identifiability concept
|
|
|
| 8 |
Nonlinear systems, an introduction
|
|
|
| 9 |
System identification methods
|
|
|
| 10 |
Parameter estimation
|
|
|
| 11 |
LS method
|
|
|
| 12 |
RLS method
|
|
|
| 13 |
Nonparametric methods
|
|
|
| 14 |
Concluding remarks
|
|
|
| |
|
|
|
Contribution of Learning Outcomes to Programme Outcomes
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https://obs.gantep.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=147951&lang=en