Mechatronics Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Mechatronics Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Mechatronics Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05571912 | Fuzzy Logic | 4 | Autumn | 7 | 3+0+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | yok |
| Mode of Delivery | Face to Face |
| Prerequisites | yok |
| Coordinator | - |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Classical Logic, Symbolic Logic, Multivalued Logic, Fuzzy sets, membership functions, fuzzy propositions, fuzzy models, fuzzy values, fuzzy quantifiers, conditional and constrained fuzzy propositions, inference of conditional and constrained fuzzy propositions, fuzzy set operations, will have the knowledge of extended fuzzy sets, fuzzy relation equations, rule base inference, fuzzification, inference mechanisms, clarification, mamdani and sugeno fuzzy system models, fuzzy relations, fuzzy functions, computer applications.
Objectives of the Course
Giving the definitions and concepts of fuzzy logic and defining applications
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P2 | Ability to identify, formulate and solve complex Mechatronics Engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 3 |
| P6 | Ability to work effectively in disciplinary and multi-disciplinary teams; individual working skills | 5 |
| P10 | Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development | 1 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Basic concepts of Machine Learning and deep learning | P.1.40 | 1 |
| O2 | To have knowledge about libraries where Deep Learning methods can be used | P.1.41 | 1 |
| O3 | Understanding the principles of Artificial Neural Networks, learning how they differ from traditional programs | P.1.42 | 1 |
| O4 | Algorithm development knowledge and creating the appropriate data structure for the algorithm | P.2.61 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | History |
| 2 | Comparison of Fuzzy Logic and Modern Logic |
| 3 | Introduction to fuzzy sets |
| 4 | Features of fuzzy sets |
| 5 | High value logic |
| 6 | High value logic |
| 7 | Fuzzy numbers, Arithmetic operations in fuzzy numbers |
| 8 | Fuzzy numbers, Arithmetic operations in fuzzy numbers |
| 9 | Midterm Exam 1 |
| 10 | Fuzzy numbers, Arithmetic operations in fuzzy numbers |
| 11 | Fuzzy Relations and Properties |
| 12 | Processes in fuzzy relations |
| 13 | Fuzzy Cartesian Multiplication |
| 14 | Tossing in Fuzzy Logic |
| 15 | Final |
Textbook or Material
| Resources | A.Kaufmann, M.M.Gupta, Introduction to Fuzzy Arithmetic,Theory and Applications,1991. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 5 | 70 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 10 | 10 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 10 | 10 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 146 | ||
| Total Work Load / 30 | 4,87 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P2 |
|---|---|---|---|
| O1 | Basic concepts of Machine Learning and deep learning | 2 | - |
| O2 | To have knowledge about libraries where Deep Learning methods can be used | - | - |
| O3 | Understanding the principles of Artificial Neural Networks, learning how they differ from traditional programs | 3 | - |
| O4 | Algorithm development knowledge and creating the appropriate data structure for the algorithm | - | 5 |