COURSE UNIT TITLE

: APPLIED MODELING WITH FUZZY LOGIC

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5055 APPLIED MODELING WITH FUZZY LOGIC ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR EFENDI NASIBOĞLU

Offered to

Ph.D. in Computer Science (English)
Computer Science
Artificial Intelligence and Intelligent Systems

Course Objective

This course aims to provide the students with an overview of the fields of applied models of fuzzy logic including methodologies and procedures. It aims to convey the key techniques of construction and using of fuzzy parameters. It also aims to provide the students computational techniques for representation and process fuzzy information in decision making problems.

Learning Outcomes of the Course Unit

1   Understanding both theoretical and practical knowledge and skills to represent fuzzy information in mathematical models.
2   Understanding of the basic mathematical techniques used for processing fuzzy information in modeling process.
3   Understanding of the basic decision making techniques with fuzzy information.
4   Understanding of the basic applied tools used in fuzzy systems.
5   Having ability to develop a practical fuzzy system.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fuzzy Logic, Soft Computing and Heuristic Algorithms
2 Fuzzy Relations, Fuzzy Transitivity, Transitive Closure, Algorithms for Transitive Closure.
3 OWA operators and Stress functions.
4 Project 1
5 Fuzzy Optimization Problems. Bin Packing with Fuzzy Matrix Constraints
6 Optimal Routing Problem with Fuzzy Logic. Fuzzy preference degrees for stop-stop, stop-line, and line-line relations.
7 Project 2
8 Midterm exam
9 Fuzzy Mathematical Programming Models
10 Project 3
11 Fuzzy Dıstance. Weighted Averaging Based on Levels (WABL) approach.
12 Fuzzy Order Relations, Fuzzy Ranking.
13 Fuzzy Nearest Approximation of a Fuzzy Number.
14 Project 4

Recomended or Required Reading

Textbook(s): Jang, J., Sun C., Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.
Supplementary Book(s): Larose D., Discovering knowledge in data: An introduction to data mining, J. Wiley & Sons, 2005.

Recent literature papers.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRJ PROJECT
2 FCG FINAL COURSE GRADE PRJ * 1


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 4 52
Preparation for final exam 1 20 20
Preparing assignments 2 25 50
Preparing presentations 2 20 40
Final 1 2 2
TOTAL WORKLOAD (hours) 203

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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