ECTS - Estimation and Identification for Engineering Systems

Estimation and Identification for Engineering Systems (MDES630) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Estimation and Identification for Engineering Systems MDES630 3 0 0 3 5
Pre-requisite Course(s)
Consent of the instructor
Course Language English
Course Type N/A
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives At the end of the course students will gain the ability to design filters for state estimation and will experience the implementation of the designed filters on physical systems. Also, some techniques in system identification will be discussed and students will practice experiments for system identification.
Course Learning Outcomes The students who succeeded in this course;
  • To develop concepts of estimation and identification for engineering systems. To give the experience of implementing estimation and identification algorithms on real experimental data.
Course Content Kalman filtering, nonparametric identification techniques and parameter estimation methods; implementation of filtering algorithms on physical systems and collecting data from real systems.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 State estimation, review of observers -
2 Kalman filter -
3 Extended Kalman filter -
4 Unscented Kalman filter -
5 Unscented Kalman filter -
6 Case studies -
7 Case studies -
8 Concepts in system identification -
9 Nonparametic methods, parameter estimation methods -
10 Least squares estimation -
11 Maximum likelihood estimation -
12 Prediction error method -
13 Neural networks for identification -
14 Case studies -
15 Overall review -
16 Final exam -


Course Book 1. Kumar, P. R., Varaiya, P., Stochastic Systems: Estimation, Identification, and Adaptive Control, Prentice Hall, 1986.
Other Sources 2. Ljung, L., System Identification, Theory for the User, PTR Prentice Hall, New Jersey, 1987.
3. Maybeck, P. S., Stochastic Models, Estimation, and Control, Academic Press, 1979.
4. Minkler G., Minkler J. Theory and Application of Kalman Filtering, Magellan Book Company, USA, 1993.
5. Nelles O., Nonlinear System Identification from Classical Approaches to Neural Networks and Fuzzy Models, Springer, 2001

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 2 40
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 20
Toplam 5 100
Percentage of Semester Work 80
Percentage of Final Work 20
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to carry out advanced research activities, both individual and as a member of a team X
2 Ability to evaluate research topics and comment with scientific reasoning X
3 Ability to initiate and create new methodologies, implement them on novel research areas and topics X
4 Ability to produce experimental and/or analytical data in systematic manner, discuss and evaluate data to lead scintific conclusions X
5 Ability to apply scientific philosophy on analysis, modelling and design of engineering systems X
6 Ability to synthesis available knowledge on his/her domain to initiate, to carry, complete and present novel research at international level X
7 Contribute scientific and technological advancements on engineering domain of his/her interest area X
8 Contribute industrial and scientific advancements to improve the society through research activities X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project 3 10 30
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 8 16
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 136