Ders Planı

Lesson Information

Course Credit	3.0
Course ECTS Credit	5.0
Teaching Language of Instruction	Türkçe
Level of Course	Bachelor's Degree, TYYÇ: Level 6, EQF-LLL: Level 6, QF-EHEA: First Cycle
Type of Course	Programme Elective
Mode of Delivery	Face-to-face
Does the course require compulsory or optional work experience?	S
Course Coordinator
Instructor (s)	Prof. Dr. ÇİĞDEM GENCER BALBİANİ
Course Assistant

Purpose and Content

The aim of the course	The aim of this course is to give the definition ,basic concepts and applications of fuzzy logic .
Course Content	This course covers multi-valued logic, fuzzy logic, fuzzy logic comparison with classical logic, the concept of membership, fuzzy sets, membership function types,fuzzy propositions,fuzzy models,fuzzy values,fuzzy quantifiers, , fuzzy conditional and restricted propsitions,inferences , level fuzzy sets, fuzzy operation sets , fuzzy extended sets , fuzzy numbers, fuzzy number operations, fuzzy set graphs, fuzzy equations,rule based inference ,fuzzification , inference mechanisms , defuzzification , Mammadani and Sugeno fuzzy system models , fuzzy relations,fuzzy functions and their basic properties , neural networks, genetic algorithms , computer applications of fuzzy logic.

The aim of the course

The aim of this course is to give the definition ,basic concepts and applications of fuzzy logic .

Course Content

This course covers multi-valued logic, fuzzy logic, fuzzy logic comparison with classical logic, the concept of membership, fuzzy sets, membership function types,fuzzy propositions,fuzzy models,fuzzy values,fuzzy quantifiers, , fuzzy conditional and restricted propsitions,inferences , level fuzzy sets, fuzzy operation sets , fuzzy extended sets , fuzzy numbers, fuzzy number operations, fuzzy set graphs, fuzzy equations,rule based inference ,fuzzification , inference mechanisms , defuzzification , Mammadani and Sugeno fuzzy system models , fuzzy relations,fuzzy functions and their basic properties , neural networks, genetic algorithms , computer applications of fuzzy logic.

Learning Outcomes

Have advanced theoretical and practical knowledge in mathematics and computer science.
Gain an in-depth knowledge on Computer Science including computer programming, word processing, database functions, accessing the internet and softwares.
Possess the knowledge of advanced research methods in mathematics-computer field.
Possess theoretical and practical knowledge in mathematics, computation and computer science.
Define computer programming, word processing, data functions, internete access and software programs.
Describe advanced research methods in the field of Mathematics-Computer Science.
Evaluate and interpret data using the knowledge and skills gained in the fields of mathematics and computer science.
Identify, define and analyze problems in the fields of mathematics and computer science; develop solutions based on research and evidence.
Identify, define and model mathematics, computation and computer science problems; select and apply appropriate analysis and modeling methods for this purpose.
Design and apply interactive experimental environments to get the definitions and first solutions of the problems of computer science and computer science and evaluate these environments.
Having the discipline of mathematics, understand the operating logic of the computer and gain the ability to think based on account.
Perform all phases of life cycle in computer based systems.
Work effectively as an individual and as a team member to solve problems in the areas of mathematics and computer science.
Have the ability to act independently, use initiative and creativity.
Work effectively either individually or in multidisciplinary teams.
Evaluate advanced knowledge and skills in the field with a critical approach.
Follow current developments about the awareness of the necessity of continuous professional development and information and communication technologies.
Have the consciousness of the necessity of lifelong learning and continuously develop professional knowledge and skills.
Use the time effectively in the process of getting the conclusion with analytical thinking ability.
Have at least one foreign language knowledge and the ability to communicate effectively in Turkish, verbally and in writing.
Be aware of the effects of information applications on individual, institutional, social and universal dimensions and have the awareness about entrepreneurship, innovation.
Have the awareness of professional and ethical responsibility and legal consequences of information applications
Use the knowledge about the field for the benefit to society.

Weekly Course Subjects

1	Fuzzy sets,membership functions and
2	Fuzzy propositions,fuzzy models,fuzzy values.
3	Fuzzy quantifiers, fuzzy conditional and restricted propsitions sets.
4	Fuzzy conditional and restricted propsitions,inferences.
5	Level fuzzy sets, fuzzy operation sets.
6	Fuzzy extended sets,fuzzy numbers and their basic properties.
7	Fuzzy relation equations,Rule based inference.
8	Fuzzification.
9	Inference mechanisms.Midterm Exam.
10	Defuzzification.
11	Mammadani and Sugeno fuzzy system models.
12	Fuzzy relations,fuzzy functions and their basic properties .
13	Neural networks, genetic algorithms.
14	Computer applications of fuzzy logic.

Resources

1.Baykal,N.&Beyan,T.,Bulanık Mantık,İlke Temelleri,Bıçakcılar Kitapevi,Ankara,2004.
2.Baykal,N.&Beyan,T.,Bulanık Mantık,Uzman Sistemler ve Denetleyiciler,Bıçakcılar Kitapevi,Ankara,2004.
3.Klir,J. G . & Yuan Bo,Fuzzy Sets and Fuzzy Logic,Theory and Applications,Prentice Hall,PTR,New Jersey,1995.
4. Dubois , D. & Prade, Henri,Fuzzy Sets and Systems,Theory and Applications,Academic Press,New York,1980.
5.Klir,J. G . & Folger, T.,,Fuzzy Sets,Uncertainty and Information,Prentice Hall,New Jersey,1988.
6 - A. Kauffmann ,Introduction to the Theory of Fuzzy Sets.

CLASS 1

CLASS 2

CLASS 3

CLASS 4

Lesson Information

Purpose and Content

Learning Outcomes

Weekly Course Subjects

Resources

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