Iğdır University Lifelong Learning Programme

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type of Course Unit	Year of Study	Semester	Number of ECTS Credits
190105000103	FUZZY LOGIC	Elective	4	8	6

Level of Course Unit

First Cycle

Objectives of the Course

Modeling modern control systems with a blurred theory. Demonstrate the application areas of fuzzy logic control systems. Growing up to have a good basic knowledge of graduate students, building a infrastructure that can adapt to technological developments.

Name of Lecturer(s)

Dr. Öğr. Üyesi Seda Aktürk

Learning Outcomes

1	At the end of this lesson, students are: The theory of blurred Clusters: Membership degree, membership function, sectional, they will have learned their basic concepts, such as blurry numbers, and processes on fuzzy clusters
2	They will be able to express a blurred suggestion using the membership function; they will learn about conditional blurred suggestions and blurry deduction methods
3	They will be aware of the use of blurry inference methods in the design of smart systems and blurry logic; robotics, control, etc. they will be familiar with their applications in the fields.
4	MATLAB will be able to solve simple blurry problems.

Mode of Delivery

Daytime Class

Prerequisites and co-requisities

Recommended Optional Programme Components

Course Contents

Introduction to the theory of fuzzy Logic and fuzzy Clusters. Operations on blurred clusters, blurry ties and blurred deductions. About the theory of judgment. Applications of fuzzy logic controllers and fuzzy systems. Artificial neural networks, sensor learning method, Delta learning method. Artificial neural networks applications. Blurry neural networks and blurred nerves. Hybrid neural networks. Blurred rule-out from digital data. Hybrid neural networks applications.

Weekly Detailed Course Contents

Week	Theoretical	Practice	Laboratory
1	Introduction to Fuzzy Logic
2	Classic Clusters and fuzzy Clusters
3
4
5
6
7
8
9
10
11
12
13
14

Recommended or Required Reading

Timothy J. Ross: «Fuzzy Logic with Engineering Applications, Zekai Şen : “ Bulanık Mantık ve Modelleme İlkeleri”

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Term (or Year) Learning Activities	Quantity	Weight
Midterm Examination	1	10
Quiz	1	90
SUM		100
End Of Term (or Year) Learning Activities	Quantity	Weight
Final Examination	1	100
SUM		100
Term (or Year) Learning Activities		40
End Of Term (or Year) Learning Activities		60
SUM		100

Language of Instruction

Turkish

Work Placement(s)

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Midterm Examination	1	1	1
Final Examination	1	1	1
Quiz	1	5	5
Laboratory	1	50	50
Self Study	1	60	60
Individual Study for Mid term Examination	1	20	20
Individual Study for Final Examination	1	46	46
TOTAL WORKLOAD (hours)			183

Contribution of Learning Outcomes to Programme Outcomes

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11	PO 12	PO 13
LO1	5	4	5	2
LO2	5	3	5	2
LO3	5	4	5	2
LO4	5	4	5	2

* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High