Syllabus Application
System Identification
EE 672
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
System Identification - EE 672
Classroom:
FENS-L058
Level of course:
Masters
Course Credits:
SU Credit:3.000, ECTS:10
Prerequisites:
-
Corequisites:
-
Course Type:
Lecture
Instructor(s) Information
Mustafa Ünel
- Email: munel@sabanciuniv.edu
Course Information
Catalog Course Description
Aims to provide the fundamental theory of identification of dynamical systems, i.e. how to use measured input-output data to build mathematical models, typically in terms of differential or difference equations. It covers: The mathematical foundations of System Identification, Non-parametric techniques, Parametrizations and model structures, Parameter estimation, Asymptotic statistical theory, User choices, Experimental design, Choice of model structure.
Course Learning Outcomes:
| 1. | - select inputs and outputs of a system, and characterize disturbances acting on the system. |
|---|---|
| 2. | - design suitable excitation signals, |
| 3. | - use measured input-output data to build mathematical models, |
| 4. | - solve linear regression problems by least squares methods, |
| 5. | - develop nonlinear NARX and Hammerstein-Wiener models |
| 6. | - preprocess data, |
| 7. | - validate obtained models |
Course Objective
-
Course Materials
Resources:
-
Technology Requirements:
-