COURSE UNIT TITLE

: SYSTEMS IDENTIFICATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5109 SYSTEMS IDENTIFICATION ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR HATICE DOĞAN

Offered to

ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM)
ELECTRICAL AND ELECTRONICS ENGINEERING
ELECTRICAL AND ELECTRONICS ENGINEERING
ELECTRICAL AND ELECTRONICS ENGINEERING

Course Objective

The objective is to build mathematical models of systems from observations of their behavior. The topics include time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction
error methods for parameter estimation, convergence, consistency, and asymptoticdistribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.

Learning Outcomes of the Course Unit

1   To be able to determine practical requirements of a system identification scheme
2   To be able to use parametric and none parametric system identification techniques
3   To be able to assess the performance of a identified system models

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to System Identification
2 Mathematical Background
3 Nonparametric Identification I
4 Nonparametric Identification II
5 Nonparametric Identification III
6 Parametric Identification I
7 Parametric Identification II
8 Parametric Identification III
9 Parametric Identification IV
10 Validation Techniques I
11 Midterm Exam
12 Validation Techniques II
13 Practical applications of system identification
14 Practical applications of system identification

Recomended or Required Reading

Ljung, Lennart.System Identification: A Theory for the User. 2nd ed. Upper Saddle
River, NJ: Prentice Hall, 1998. ISBN: 0136566952.

Planned Learning Activities and Teaching Methods

Lectures with active participation, classical midterm and final examinations, homeworks.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 + ASG *0.25 +FIN *0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG *0.25 +RST *0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Learning outcomes are evaluated by homeworks and examinations.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

E-mail: hatice.dogan@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparation for midterm exam 1 20 20
Preparation for final exam 1 24 24
Preparing assignments 5 10 50
Preparations before/after weekly lectures 13 4 52
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 189

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.15144212
LO.25144212
LO.351442121

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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