Course Information
Course Title Code Semester T + P ECTS
Introduction to Fuzzy Logic and Artificial Neural Network BMM423 7 3 + 0 6

Prerequisites None

Language Turkish
Level Bachelor's Degree
Type Area Elective
Coordinator Assoc.Prof. MUHAMMET UZUNTARLA
Instructors
Goals Fuzzy logic models logic inference systems with the aim of the complex non-linear. solution Artificial neural networks (ANN) method of the brain perform a specific task or function / path designed to model structures. This course provides basic information on fuzzy logic and neural networks and offer application examples.
Contents Fuzzy Sets. Membership functions. Fuzzy Operations. T-norm and S-norm operations. Fuzzy rules. Blur, clarification, the fuzzy inference. Mamdani fuzzy inference. Mamdani fuzzy inference applications. Sugeno fuzzy inference and application instance. Matlab fuzzy logic applications. The structure of the brain. Neural. Perceptron. Multi-layer neural networks. Learning. Back-propagation algorithm. Momentum coefficient. Matlab neural network applications.
Work Placement(s) Absent

Number Learning Outcomes
1 Comprehend the basics of fuzzy logic.
2 Comprehend the basics of artificial neural networks,
3 Comprehend how to apply fuzzy logic and neural network structures in problems encountered.
4 Can learn fuzzy inference methods commonly used.
5 Can learn example of fuzzy logic and neural network the software tools.

Mode of Delivery Face-to-Face
Planned Learning Activities & Teaching Methods Theoretical and interactive narrative
Assessment Methods Midterm exam, the final exam



Course Content
Week Topics Study Materials
1 Fuzzy Sets. Membership functions. Read on the subject.
2 Fuzzy Operations. T-norm and S-norm operations. Read on the subject.
3 Fuzzy rules. Blur, clarification, the fuzzy inference. Read on the subject.
4 The Mamdani fuzzy inference. Read on the subject.
5 The Mamdani fuzzy inference applications. Read on the subject.
6 Sugeno fuzzy inference and application examples Read on the subject.
7 In Matlab environment fuzzy logic applications. Read on the subject.
8 The structure of the brain. Artificial neuron. Read on the subject.
9 Midterm Read on the subject.
10 Perceptron, Multi-layer neural networks. Read on the subject.
11 Learning Read on the subject.
12 Back-propagation algorithm. Read on the subject.
13 Back-propagation momentum coefficient. Read on the subject.
14 In Matlab environment ANN applications. Read on the subject.



Sources
Textbook 1-J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro Fuzzy and Soft Computing, Prentice Hall, Upper Sllade River, NJ 07458, 1997 2-Nazife Baykal, Timur Beyan, Bulanık Mantık İlke ve Temelleri, Seçkin Yayınları, 2004, Ankara
Additional Resources 1-S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan Publishing Company, Englewood Cliffs, NJ, 1994



Assessment System Quantity Percentage
In-Term Studies
Mid-terms 1 100
In-Term Total 1 100
Contribution of In-Term Studies to Overall 40
Contribution of Final Exam to Overall 60
Total 100





Course's Contribution to PLO
No Key Learning Outcomes Level
1 2 3 4 5
1 Has principal knowledge about Math, Science and Engineering subjects related to their own branches. x
2 Uses Math, Science and theoretical and practical knowledge of their own areas to find solutions for current engineering problems. x
3 Identifies, formulates and solves engineering problems, for that purpose, selects and applies appropriate analytical methods and modeling techniques. x
4 Analyzes a system, a component, or a process, designs under realistic constraints to meet the desired requirements; implements the methods of modern design accordingly. x
5 Selects and uses modern techniques and tools necessary for engineering applications. x
6 Designs and performs experiments, collects data, analyzes and interprets the results. Finds solutions to problems in the fields of medicine and biology using engineering techniques. x
7 Works effectively as an individual and multidisciplinary teams. x
8 Collects information and does research of resources for this purpose, uses databases and other information resources. x
9 Is aware of necessity of lifelong learning; monitors developments in science and technology and continuously renews himself/herself. x
10 Uses informatics and communication technology with computer software that is minimum required by the European Computer Driving Licence Advanced Level. x
11 Communicates effectively verbal and written, uses at least one foreign language at B1 level of European Language Portfolio. x
12 Is aware of universal and social effects of engineering solutions and applications, Is aware of entrepreneurship and innovation and has knowledge of contemporary issues. x
13 Has principal knowledge about professional and ethical responsibility. x
14 Holds awareness about project management, workplace practices, employee health, environmental and occupational safety; and about the legal implications of engineering applications. x
15 Trains individuals to be preferred in biomedical industry by national and international institutions and have the qualification of hardware. x
16 Provides training and consulting services to improve the quality and reliability in the use of technology in hospitals in the field of clinical engineering. x
17 Provides consulting and technical support services to hospitals, health organizations and medical technology manufacturers/sellers. x



ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities Quantity Duration (Hour) Total Work Load (h)
Course Duration 14 3 42
Hours for off-the-classroom study (Pre-study, practice) 14 4 56
Assignments 6 2 12
Projects 1 15 15
Mid-terms 1 20 20
Final examination 1 25 25
Total Work Load (h) 170
Total Work Load / 30 (h) 5.67
ECTS Credit of the Course 6