Fall 2017
MKT 421:
INTRODUCTION TO FUZZY LOGIC
Instructor: H.Metin ERTUNÇ, PhD E-mail: [email protected]
Web: http://mekatronik.kocaeli.edu.tr/hmertunc http://sites.google.com/site/hmertunc
(You may access the up-dated and lecture notes form these page.) Lecture hours: Thursday: 09:00 – 11:50 (I.education)
Thursday 17:00 – 19:50 (II.education)
Lab: There is no laboratory for this class. However, homeworks at Matlab and Simulink environment will be assigned. Besides, there is a final term project.
Office hours: Thursday: 15:00 – 17:00 Objective of the lecture:
1. To learn the concept of fuzziness.
2. To gain the ability of decision making and controlling of the systems using fuzzy logic. Course prerequisites:
1. Familiarity with Boolean logic and set theory.
2. Expertise in coding and using of Matlab/Simulink software program.
Attendance: Attendance at least 70% is required. It will not be easy to pass this course without attending class.
Grading: Ratio of the activities in the semester to total success: 60% Ratio of the final exam to total success: 40%
Grading of workloads in the Semester: Midterm: 50% Homeworks: 20% Term project: 30% References:
1. Fuzzy Logic with Engineering Applications, Timothy Ross, Wiley.
2. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, J.S.R. Jang, C.T. Sun, E. Mizutani, Prentice Hall, 1996
3. A First Course in Fuzzy Logic, H.T.Nguyen, E.A. Walker, Chapman&Hall, 2006. 4. Fuzzy Logic Toolbox For Use With Matlab, Users Guide, Mathworks.
5. Bulanık Mantık Uzman Sistemler ve Denetleyiciler, Nazife Baykal, Bıçaklar Kitabevi, 2004. 6. Bulanık Mantık İlke ve Temelleri, Nazife Baykal, Bıçaklar Kitabevi, 2004.
7. Mühendislikte Bulanık (Fuzzy) Mantık ile Modelleme Prensipleri, Zekai Şen, Su Vakfı Yayınları, 2004 8. Bulanık Mantık Denetleyiciler, Çetin Elmas, Seçkin Yayıncılık, 2003.
9. The lecture notes that will be delivered in the class 10. Tutorials that may be downloaded from the internet.
Contents: This course covers; classical sets and fuzzy set theorem, fuzzy logic principals, the basic structure of fuzzy logic controllers, system variables and fuzzy parameters, fuzzification methods, the construction of rule tables, fuzzy inference and defuzzification techniques, the design of fuzzy logic controllers, case studies and applications using fuzzy logic controllers.
