| Course Name | Code | Semester | T+U Hours | Credit | ECTS |
|---|---|---|---|---|---|
| Fuzzy Logic and Artificial Neural Network | ISE 431 | 7 | 3 + 0 | 3 | 5 |
| Precondition Courses | |
| Recommended Optional Courses | |
| Course Language | Turkish |
| Course Level | Bachelor's Degree |
| Course Type | Optional |
| Course Coordinator | Dr.Öğr.Üyesi TUĞRUL TAŞCI |
| Course Lecturers | |
| Course Assistants | |
| Course Category | |
| Course Objective | The fuzzy logic has the capability of solving complex non-linear system using human intelligence and reasoning model. Neural Networks are used for modelling of the brain functions to solve complex non-linear system. This course presents basic knowledge about fuzzy logic, neural Networks and applications |
| Course Content | Fuzzy sets. Membership functions. Fuzzy operations. T-norm, N- norm operator. Fuzzy Rules Fuzzification, defuzzification. Fuzzy inferrence. Mamdani fuzzy inference. Mamdani fuzzy inference applications. Sugenoi fuzzy inference and applications. Matlab fuzzy applications. The structure of the brain. Artificial Neuron. Perceptron. Multilayer neural networks. Learning. Back propagation algorithm. Momentum coefficient. Matlab neural network applications |
| # | Course Learning Outcomes | Teaching Methods | Assessment Methods |
|---|---|---|---|
| 1 | Understand basic knowledge about fuzzy logic | Lecture, Question-Answer, | Testing, Oral Exam, |
| 2 | Understand basic knowledge about neural Networks | Lecture, Question-Answer, Drilland Practice, | Testing, Homework, |
| 3 | Understand using the fuzzy logic and ANN for encountered problems | Lecture, Question-Answer, Drilland Practice, | Testing, Homework, |
| 4 | Comrehend common fuzzy inference methods | Lecture, Question-Answer, Discussion, | Testing, Homework, |
| 5 | Comprehend sample fuzzy logic and ANN tools | Lecture, Question-Answer, Discussion, | Testing, Oral Exam, Homework, |
| Week | Course Topics | Preliminary Preparation |
|---|---|---|
| 1 | Fuzzy sets. Membership functions | |
| 2 | Fuzzy operations. T-norm, N- norm operator | |
| 3 | Fuzzy Rules Fuzzification, defuzzification. Fuzzy inferrence | |
| 4 | Mamdani fuzzy inference | |
| 5 | Mamdani fuzzy inference applications | |
| 6 | Sugenoi fuzzy inference and applications | |
| 7 | Matlab fuzzy applications | |
| 8 | The structure of the brain. Artificial Neuron | |
| 9 | Perceptron | |
| 10 | Multilayer neural networks | |
| 11 | Learning | |
| 12 | Back propagation algorithm | |
| 13 | Momentum coefficient | |
| 14 | Matlab neural network applications |
| Resources | |
|---|---|
| Course Notes | Fuzzy Logic and NN, Sakarya University, Notes |
| Course Resources | 1.J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro Fuzzy and Soft Computing, Prentice Hall, Upper Sllade River, NJ 07458, 1997 2.S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan Publishing Company, Englewood Cliffs, NJ, 1994 3.Nazife Baykal, Timur Beyan, Bulanık Mantık İlke ve Temelleri, Seçkin Yayınları, 2004, Ankara |
| Order | Program Outcomes | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | X | ||||||
| 2 | X | ||||||
| 3 | X | ||||||
| 4 | X | ||||||
| 5 | X | ||||||
| 6 | X | ||||||
| 7 | X | ||||||
| 8 | X | ||||||
| 9 | X | ||||||
| 10 | X | ||||||
| 11 | X | ||||||
| 12 | X | ||||||
| Evaluation System | |
|---|---|
| Semester Studies | Contribution Rate |
| 1. Ara Sınav | 50 |
| 1. Kısa Sınav | 20 |
| 1. Ödev | 15 |
| 2. Ödev | 15 |
| Total | 100 |
| 1. Yıl İçinin Başarıya | 10 |
| 1. Final | 90 |
| Total | 100 |
| ECTS - Workload Activity | Quantity | Time (Hours) | Total Workload (Hours) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 1 | 3 | 3 |
| Quiz | 1 | 3 | 3 |
| Assignment | 2 | 3 | 6 |
| Final examination | 1 | 8 | 8 |
| Total Workload | 116 | ||
| Total Workload / 25 (Hours) | 4.64 | ||
| dersAKTSKredisi | 5 | ||