Course Name Code Semester T+U Hours Credit ECTS
Fuzzy Logic and Artificial Neural Network EBT 542 0 3 + 0 3 6
Precondition Courses <p>N/a</p>
Recommended Optional Courses <p>N/A</p>
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Dr.Öğr.Üyesi MUHAMMED FATİH ADAK
Course Lecturers Dr.Öğr.Üyesi MUHAMMED FATİH ADAK,
Course Assistants

Yrd. Doç. Dr. Seçkin Arı

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, Project / Design,
2 Understand basic knowledge about neural Networks Lecture, Question-Answer, Testing, Project / Design, Performance Task,
3 Understand using the fuzzy logic and ANN for encountered problems Lecture, Question-Answer, Brain Storming, Testing, Oral Exam, Homework,
4 Comrehend common fuzzy inference methods Lecture, Problem Solving, Testing, Oral Exam, Homework,
5 Comprehend sample fuzzy logic and ANN tools Lecture, Question-Answer, Self Study, Oral Exam, Homework, Performance Task,
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
Course Notes <p>Lecturer notes for the course will be post on course web page&nbsp;</p>
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 ability to access wide and deep information with scientific researches in the field of Engineering, evaluate, interpret and implement the knowledge gained in his/her field of study X
2 ability to complete and implement limited or incomplete data by using the scientific methods. X
3 ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them X
4 ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process X
5 gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering X
6 ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process X
7 gain high level ability to define the required information and data X
8 ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations X
9 systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
10 aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement X
11 aware of new and developing application of profession and ability to analyze and study on those applications X
12 ability to interpret engineering applications social and environmental dimensions and its compliance with the social environment
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 30
1. Ödev 35
2. Ödev 35
Total 100
1. Yıl İçinin Başarıya 40
1. Final 60
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 25 25
Final examination 1 20 20
Total Workload 141
Total Workload / 25 (Hours) 5.64
dersAKTSKredisi 6