Department of Electrical and Computer Engineering
Faculty of Engineering and Architecture
American University of Beirut
EECE 644: Fuzzy Sets, Logic and Applications
Catalog description
Fuzzy sets and related concepts, logical connectives, mapping of fuzzy sets, extension
principle, fuzzy relations, fuzzy logic and inference systems, concepts in intelligent
system design, fuzzy arithmetic, applications related to fuzzy control, robotics and
other areas.
Credit hours: 3 credits
Required or elective
Elective for undergraduate and graduate ECE and CCE students.
Prerequisites
General: 3rd year, senior or graduate standing in CCE, ECE or a related field.
By Topic: Basic set theory and concepts related to functions, relations, etc.
Knowledge in classical logic is a plus but not a requirement.
References
1.Fuzzy Logic with Engineering Applications, T.J. Ross, Wiley, 2009.
2.Fuzzy Control, Farinwata, Filev and Langari, Wiley, 2000.
3.Fuzzy Sets and Fuzzy Logic: Theory and Applications, G. Klir and Bo Yuan,
Prentice Hall, 1995.
4.Fuzzy Sets Engineering, Witold Pedrycz, CRC Press, 1995.
Course objectives
1. An understanding of the basic mathematical elements of the theory of fuzzy
sets.
2. An emphasis on the differences and similarities between fuzzy sets and
classical sets theories.
3. Coverage of fuzzy logic inference with emphasis on their use in the design of
intelligent or humanistic systems.
4. A brief introduction to fuzzy arithmetic concepts.
5. An insight into fuzzy inference applications in the area of control and robotics.
Course Topics
1. Review of classical set theory and related concepts
2. Fuzzy sets and related concepts; membership functions, operations, algebra,
etc.
3. Mapping fuzzy sets and extension principle
4. Fuzzy numbers
5. Fuzzy relations
6. Fuzzy logic and relationship to binary logic
7. Fuzzy propositions-classical propositions and classical inference
8. Fuzzy inference using conditional propositions – Fuzzy inference systems
9. Learning algorithms for intelligent systems design
10. Fuzzy arithmetic concepts
11. Applications in robotics, control, etc.
Course Learning Outcomes
1. Be able to distinguish between the crisp set and fuzzy set concepts through the
learned differences between the crisp set characteristic function and the fuzzy
set membership function.
2. Be able to draw a parallelism between crisp set operations and fuzzy set
operations through the use of characteristic and membership functions
respectively.
3. Be able to define fuzzy sets using linguistic words and represent these sets by
membership functions.
4. Know how to perform mapping of fuzzy sets by a function and also use the αlevel sets in such instances.
5. Know fuzzy-set-related notions; such as α-level sets, convexity, normality,
support, etc.
6. Know the concept of a fuzzy number and how it is defined.
7. Become familiar with the extension principle, its compatibility with the αlevel sets and the usefulness of the principle in performing fuzzy number
arithmetic operations (Additions, multiplications, etc.)
8. Become familiar with fuzzy relations and the properties of these relations.
9. Become capable of drawing a distinction between binary logic and fuzzy logic
at the conceptual level.
10. Become capable of representing a simple classical proposition using crisp set
characteristic function and likewise representing a fuzzy proposition using
fuzzy set membership function.
11. Become knowledgeable of conditional fuzzy propositions and fuzzy inference
systems.
12. Become aware of the use of fuzzy inference systems in the design of
intelligent or humanistic systems.
13. Become aware of the application of fuzzy inference systems in control,
robotics, etc.
14. Have acquired the ability of thinking differently and have become capable,
when necessary, to apply a new thinking methodology to real life problems
including engineering ones.
Class schedule
Two 75-minutes lectures per week
Resources of the course
References, course notes, previous Exams and solutions, e-reserve material.
Evaluation methods
Midterm (30%), final exam (40%), project (20%), class attendance (10%)
Professional component
Engineering topics: 40%
General education: 10%
Mathematics and basic sciences: 50%
Computer usage
Matlab, Labview fuzzy logic tools
Person(s) who prepared this syllabus
Dr. Jean J. Saade
Date this syllabus is updated:
February 4, 2013.