COURSE UNIT TITLE

: FUZZY DECISION SYSTEMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIL 3002 FUZZY DECISION SYSTEMS COMPULSORY 4 0 0 7

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASISTANT PROFESSOR AYŞE ÖVGÜ KINAY

Offered to

Computer Science

Course Objective

The objective of the course is to construct fuzzy decision systems, to teach the basic structure of fuzzy logic and to gain ability of design of fuzzy logic controllers.

Learning Outcomes of the Course Unit

1   Be able to construct fuzzy decision making models.
2   Be able to design fuzzy inference systems.
3   Be able to use cluster analysis.
4   Be able to perform fuzzy pattern recognition procedures.
5   Be able to decision making with fuzzy information.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fuzzy Set Definition and Basic Operations
2 Fuzzy Inference Systems
3 Fuzzy Inference Systems (cont.)
4 Fuzzy Inference Systems (cont.)
5 Decision Making with Fuzzy Information Fuzzy Ordering, Nontransitive Ranking, Preference and Consensus
6 Multiattribute Decision Making Methods
7 Multiattribute Decision Making Methods (cont.)
8 Mid-term exam
9 Fuzzy Classification, Crisp and Fuzzy Relations, Cluster Analysis, Cluster Validity
10 c-Means Clustering, Hard c-Means, Fuzzy c-Means
11 c-Means Clustering, Hard c-Means, Fuzzy c-Means (cont.)
12 Classification metric, Hardening the Fuzzy c-Partition, Similarity Relations from Clustering
13 Fuzzy Pattern Recognition
14 Fuzzy Pattern Recognition (cont.)

Recomended or Required Reading

Textbook(s):
Ross, T.J., Fuzzy Logic with Engineering Applications, McGraw-Hill, 1995.
Supplementary Book(s):
Lin, C.T. and George Lee, C.S., Neural Fuzzy Systems, Prentice Hall, 1996.
Pedrycz, W., An Introduction to Fuzzy Sets, Massachusets Ins. of Technology, 1998.
Klir, G.J. and Folger, T.A., Fuzzy Sets, Uncertainty and Information, Prentice Hall, 1988.
Cox, E., Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, Morgan Kaufmann Publishers, 2005.

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 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.30 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

ovgu.tekin@deu.edu.tr
efendi.nasibov@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 4 52
0
0
Preparations before/after weekly lectures 12 2 24
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Preparing assignments 2 25 50
Final 1 2 2
Midterm 1 2 2
Project Assignment 2 2 4
TOTAL WORKLOAD (hours) 169

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.1354
LO.2354
LO.3355444
LO.4554
LO.5354

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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