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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6083 FUZZY LOGIC ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

Offered to

Econometrics

Course Objective

The main objective of the course is teach theory of fuzzy logic set, basic structures of fuzzy logic inspectors, to design fuzzy logic inspectors and make a practice of fuzzy logic in decision analysis

Learning Outcomes of the Course Unit

1   To be able to make explain difference between theory of sharp set and theory of fuzzy logic 2   To be able to distinguish basic structures of fuzzy logic inspectors. 3   To be able to use conceive methods of fuzziness, forming of data base, techniques of fuzzy judgement andd methods of clarification. 4   To be able to design inspector of fuzzy logic. 5   To be able to make a practice of simulation of a dynamic system using fuzzy inspector.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Fuzzy Logic Sharp Set and Theory of Fuzzy Set 2 Principles of Fuzzy Logic Basic structure of Fuzzy Logic inspectors 3 System variables and fuzzy parameters Strategies of Fuzziness, forming of data set 4 Techniques of Fuzzy judgement 5 Implementations of Fuzzy Logic 6 Implementations of Fuzzy Logic 7 Strategies of clarification and design of Fuzzy Control s rules 8 Mid-term 9 Design and samples related to Fuzzy Logic inspectors 10 To Obtain Mathematical model of a dynamic system 11 Design of fuzzy logic inspector and inspection of a dynamic system 12 Design of fuzzy logic inspector and simulation of inspection of a dynamic system 13 Design of fuzzy logic inspector and simulation of inspection of a dynamic system 14 To evaluate term projects

Recomended or Required Reading

1- Yan, J., Ryan, M., Power, J., (1994), Using Fuzzy Logic, Prentice Hall 2- Fuzzy Logic with Engineering, Timothy Ross , Timothy J. Ross, John Wiley & Sons , 2009 3- Elmas, C., (2007) Yapay Zeka Uygulamaları, Seçkin Yayınevi

Planned Learning Activities and Teaching Methods

This course will be presented using methods of expression, discussion and solving problem.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 3 39 Preparation for midterm exam 1 35 35 Preparation for final exam 1 35 35 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 154

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1