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Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
INTRODUCTION TO FUZZY LOGIC AND ARTIF. NEURAL NET. BSM 427 7 3 + 0 3 5
Ön Koşul Dersleri
Önerilen Seçmeli Dersler
Dersin Dili Türkçe
Dersin Seviyesi Lisans
Dersin Türü SECMELI
Dersin Koordinatörü Dr.Öğr.Üyesi MUHAMMED FATİH ADAK
Dersi Verenler Dr.Öğr.Üyesi MUHAMMED FATİH ADAK
Dersin Yardımcıları
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 - 10 - A - B - C -
4 - Comrehend common fuzzy inference methods 1 - 2 - 10 - A - B - C -
5 - Comprehend sample fuzzy logic and ANN tools 1 - 2 - A - D -
Öğretim Yöntemleri: 1:Lecture 2:Question-Answer 10:Brain Storming
Ölçme Yöntemleri: A:Testing D:Project / Design F:Performance Task B:Oral Exam C:Homework

Ders Akışı

Hafta Konular ÖnHazırlık
1 Introduction
2 Classical Sets, Fuzzy Sets
3 Classical and Fuzzy Relations
4 Membership functions, Fuzzification and Defuzzyfication
5 Mamdani fuzzy inference and rules
6 Sugenoi fuzzy inference and rules
7 Introduction to Jfuzzylogic Library and Sample Codes
8 Engineering Applications by Jfuzzylogic Library
9 Human brain and Artificial Neural
10 Perceptron Concept and Learning
11 Multi Layer Neural Networks
12 Back Propagation Algorithm
13 Introduction ANN Library in Java
14 Engineering Applications by ANN Library in Java

Kaynaklar

Ders Notu Lecture Notes Sakarya Universty
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

Döküman Paylaşımı


Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5
1 To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions, X
2 An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations X
3 An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently, X
4 An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods, X
5 An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research, X
6 Understanding, awareness of administration, control, development and security/reliability issues about information technologies, X
7 An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility, X
8 An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language, X
9 An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology, X
10 Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society,
11 An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications,
12 An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems.

Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 30
KisaSinav 1 15
KisaSinav 2 20
Odev 1 20
Odev 2 15
Toplam 100
Yıliçinin Başarıya Oranı 60
Finalin Başarıya Oranı 40
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 5 5
Quiz 2 3 6
Assignment 2 10 20
Final examination 1 10 10
Toplam İş Yükü 137
Toplam İş Yükü /25(s) 5.48
Dersin AKTS Kredisi 5.48
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