Course Name Code Semester T+U Hours Credit ECTS
Introduction To Fuzzy Logic and Artif. Neural Net. BSM 427 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 MUHAMMED FATİH ADAK
Course Lecturers Dr.Öğr.Üyesi MUHAMMED FATİH ADAK,
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, 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, Brain Storming, Testing, Oral Exam, Homework,
4 Comrehend common fuzzy inference methods Lecture, Question-Answer, Brain Storming, Testing, Oral Exam, Homework,
5 Comprehend sample fuzzy logic and ANN tools Lecture, Question-Answer, Testing, Project / Design,
Week Course Topics Preliminary Preparation
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
Course Notes Lecture Notes Sakarya Universty
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 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.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 30
1. Kısa Sınav 20
1. Ödev 25
2. Ödev 25
Total 100
1. Yıl İçinin Başarıya 60
1. Final 40
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 5 5
Quiz 2 3 6
Assignment 2 10 20
Final examination 1 10 10
Total Workload 137
Total Workload / 25 (Hours) 5.48
dersAKTSKredisi 5