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