COURSE INFORMATION Course Title: FUZZY LOGIC

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 874	C	99	3	2	0	4	7.5

Language:	English
Compulsory/Elective:	Elective
Classroom and Meeting Time:
Course Description:	-
Course Objectives:	This course introduces the students to the theoretical foundations of fuzzy logic and some of its main applications. The students will learn the concepts of fuzzy sets, membership functions, fuzzy relations, fuzzy inference rules, fuzzy systems etc. Moreover several important applications will be seen in the prospect of control systems, machine learning and generally dealing with imprecision and non-random uncertainty in real-world scenarios.

COURSE OUTLINE

Week	Topics
1	Introduction of Fuzzy Logic and Neural Networks as intelligent techniques to manipulate imprecise and approximated data and systems. Industrial applications. Intelligent control of complex systems
2	Fuzzy sets theory and concepts, membership functions, operations on fuzzy sets, triangular norms
3	Fuzzy relations and fuzzy quantities, fuzzy intervals, fuzzy numbers, operation on fuzzy quantities
4	Linguistic variables, linguistic modifiers, fuzzy rules, fuzzy quantifiers
5	Fuzzy reasoning, fuzzy implications, generalized modus ponens
6	Fuzzy control, Mamdani and Larsen methods. Applications
7	Exercises on Fuzzy Logic
8	Matlab simulations using the Fuzzy Toolbox
9	Learning process, error correction learning algorithm, Hebb learning algorithm, competitive learning
10	Multi-layer perceptron. Applications. Back-propagation algorithm. Momentum. Adaptive learning rate. Levenberg-Marquardt learning algorithm. Cross validation technique
11	Generalized back-propagation algorithms 1
12	Generalized back-propagation algorithms 2
13	Application of neural networks in the identification and control of complex dynamic systems
14	Review

Prerequisite(s):
Textbook:	"Fuzzy logic with engineering applications.", Timothy J. Ross, 3rd edition, John Wiley & Sons, 2009.
Other References:	"Fuzzy logic - An Introductory Course for Engineering Students", Enric Trillas, Luka Eciolaza, Springer 2015
Laboratory Work:
Computer Usage:
Others:	No

COURSE LEARNING OUTCOMES

1	To learn the theoretical foundations of fuzzy logic including: fuzzy sets, fuzzy relations, fuzzy inference rules etc.
2	To know the operational principles of the fuzzy control systems.
3	To know the applications of fuzzy logic in within the machine learning framework.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Doctorate (PhD) in Computer Engineering Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	4
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	4
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	5
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	5
8	Ability for effective oral and official communication skills in foreign language.	3
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	5
10	Engineering graduates with well-structured responsibilities in profession and ethics.	5
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	4
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	30
Presentation	1	20
Term Paper	1	50
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	4	64
Hours for off-the-classroom study (Pre-study, practice)	15	2	30
Mid-terms			0
Assignments			0
Final examination	1	28.5	28.5
Other	1	65	65
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5