Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
190105000103FUZZY LOGICElective486
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
1At 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
2They will be able to express a blurred suggestion using the membership function; they will learn about conditional blurred suggestions and blurry deduction methods
3They 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.
4MATLAB 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
WeekTheoreticalPracticeLaboratory
1Introduction to Fuzzy Logic
2Classic 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 ActivitiesQuantityWeight
Midterm Examination110
Quiz190
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Language of Instruction
Turkish
Work Placement(s)
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination111
Final Examination111
Quiz155
Laboratory15050
Self Study16060
Individual Study for Mid term Examination12020
Individual Study for Final Examination14646
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
LO15452         
LO25352         
LO35452         
LO45452         
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
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