İzmir Ekonomi Üniversitesi
  • TÜRKÇE

  • GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    MATH 671 | Course Introduction and Application Information

    Course Name
    Fuzzy Optimization
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    MATH 671
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Third Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator -
    Course Lecturer(s)
    Assistant(s) -
    Course Objectives Fuzzy Set Theory is the approach to solve the problems that cannot be solved by classical set theory or probability theory. In this course, Fuzzy Set Theory and the basis of fuzyy logic will be examined. It also describes, fuzzy logic applications such as fuzzy control and fuzzy decision making, disucced in the areas of optimization.
    Learning Outcomes

    The students who succeeded in this course;

    • Be able to examine the Set Theory problems.
    • Be able to interpret the systems which include fuzzines within the scope of fuzzy set theory .
    • Be able to combine the information of decision theory and the information of fuzzy set theory.
    • Be able to improve the proof techniques of Fuzzy Set Theory.
    • Be able to solve problems that include uncertainty with using Fuzzy Set Theory.
    Course Description The course covers basic concepts and applications of Fuzzy Set Theory.

     



    Course Category

    Core Courses
    Major Area Courses
    Supportive Courses
    Media and Management Skills Courses
    Transferable Skill Courses

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Related Preparation Learning Outcome
    1 Fuzzy Sets Basic Definitions Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    2 Fuzzy Sets Basic Definitions Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    3 Fuzzy Measures and Fuzziness Measurements Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    4 Fuzzy Measures and Fuzziness Measurements Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    5 Fuzzy Relations and Fuzzy Graphics Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    6 Fuzzy Relations and Fuzzy Graphics Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    7 Possibility Theory, Probability Theory and Fuzzy Set Theory Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    8 Possibility Theory, Probability Theory and Fuzzy Set Theory Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    9 Fuzzy Logic Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    10 Midterm Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    11 Decision Makig in Fuzzy Environment Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    12 Decision Makig in Fuzzy Environment Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    13 Decision Makig in Fuzzy Environment Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    14 Decision Makig in Fuzzy Environment Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    15 Review Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc.
    16 Review of the Semester  

     

    Course Notes/Textbooks Some chapters and exercises of the above books will be used.
    Suggested Readings/Materials Fuzzy Logic with Engineering Applications by T.J. Ross, McGrawHill Book Company, 1995.

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    Presentation / Jury
    1
    30
    Project
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    30
    Final Exam
    1
    40
    Total

    Weighting of Semester Activities on the Final Grade
    60
    Weighting of End-of-Semester Activities on the Final Grade
    40
    Total

    ECTS / WORKLOAD TABLE

    Semester Activities Number Duration (Hours) Workload
    Theoretical Course Hours
    (Including exam week: 16 x total hours)
    16
    3
    48
    Laboratory / Application Hours
    (Including exam week: '.16.' x total hours)
    16
    0
    Study Hours Out of Class
    15
    6
    90
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    0
    Presentation / Jury
    1
    0
    Project
    1
    30
    30
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    30
    30
    Final Exam
    1
    30
    30
        Total
    228

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    #
    PC Sub Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.
    -
    -
    -
    -
    X
    2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
    -
    -
    -
    X
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.
    -
    -
    -
    -
    X
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    X
    -
    5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    -
    -
    X
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.
    -
    -
    -
    -
    X
    7 Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.
    -
    X
    -
    -
    -
    8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.
    -
    -
    X
    -
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    -
    X
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    X
    -
    -
    11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.
    -
    X
    -
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    X
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     


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