Ders Bilgileri

Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
FUZZY LOGIC AND ARTIFICIAL NEURAL NETWORK EBT 542 0 3 + 0 3 6
Ön Koşul Dersleri N/a N/A
 Dersin Dili Türkçe Dersin Seviyesi Yüksek Lisans Dersin Türü SECMELI Dersin Koordinatörü Dr.Öğr.Üyesi MUHAMMED FATİH ADAK Dersi Verenler Dersin Yardımcıları Yrd. Doç. Dr. Seçkin Arı Dersin Kategorisi Dersin Amacı 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 Dersin İçeriği 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
 Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri 1 - Understand basic knowledge about fuzzy logic 1 - 2 - A - D - 2 - Understand basic knowledge about neural Networks 1 - 2 - A - D - F - 3 - Understand using the fuzzy logic and ANN for encountered problems 1 - 2 - 10 - A - B - C - 4 - Comrehend common fuzzy inference methods 1 - 15 - A - B - C - 5 - Comprehend sample fuzzy logic and ANN tools 1 - 2 - 14 - B - C - F -
 Öğretim Yöntemleri: 1:Lecture 2:Question-Answer 10:Brain Storming 15:Problem Solving 14:Self Study Ölçme Yöntemleri: A:Testing D:Project / Design F:Performance Task B:Oral Exam C:Homework

Ders Akışı

Hafta Konular ÖnHazırlık
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

Kaynaklar

Ders Notu

Lecturer notes for the course will be post on course web page

Ders Kaynakları

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

Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
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

Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 30
Odev 1 35
Odev 2 35
Toplam 100
Yıliçinin Başarıya Oranı 40
Finalin Başarıya Oranı 60
Toplam 100

AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
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
Toplam İş Yükü 141
Toplam İş Yükü /25(s) 5.64
Dersin AKTS Kredisi 5.64
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