Identification
Prerequisites
Language
Compulsory/Elective
Required textbooks
and course materials
Subject
Department
Program
Term
Instructor
E-mail:
Phone:
Classroom/hours
Office hours
CMS 415
English
Required
Core textbook:
CMS 425 – Soft Computing – 3KU /6ECTS credits
Computer Science
Undergraduate
Spring, 2013
Associate Professor Leyla Muradkhanli
leyla@khazar.org
(+994 12) 421-10-93 (ext. 227)
11 Mehseti str. (Neftchilar campus), Room #401N, Monday
18:00-20:30
Monday, 16:00 – 17:30 or by appointment
Soft Computing, D. K. Pratihar, Alpha Science International Ltd, 2008.
Supplementary textbook:
Neural Networks and learning machines / Simon Haykin, Prentice Hall, 2009.
Web Resources :
Links to pages around the web with information on Fuzzy Sets and Systems,
Professional Organizations and Networks, Fuzzy Logic Journals and Books and
Research groups :
http://www.abo.fi/~rfuller/fuzs.html
Lotfi A.Zadeh the founder of Fuzzy Logic :
http://www.cs.berkeley.edu/~zadeh/
Course website
Course outline
This course combines traditional face-to-face classes with online learning. The course
management platform Moodle is used to provide a wide range of resources to support
learning. And all course related materials including, but not limited to, syllabus,
supplementary readings, course announcements, cases and assignments are available
only at the course website http://www.khazar.org/moodle. Grades will also be posted
on Moodle. The students are expected to check it n a regular basis and communicate
with the lecturer only via Moodle.
This course provides an introduction to the basic concepts of Soft Computing
methodology and covers three main components - Fuzzy Logic, Neural
Networks, and Evolutionary Computation.
The course combines theoretical foundations with practical applications using
different tools and techniques.
Topics include Fuzzy Logic, Neural Networks, Evolutionary Computation and
recent developments and applications of Soft Computing in various areas.
Course objectives
Generic Objective of the Course:
To develop an understanding of the basic concepts of Soft Computing methodology.
Specific Objectives of the Course:
 To introduce the ideas of fuzzy sets, fuzzy logic and fuzzy inference system;
 To familiarize with neural networks and learning methods for neural networks;
 To introduce basics of genetic algorithms and their applications in optimization
and planning;
 To introduce students tools and techniques of Soft Computing;
 To develop skills thorough understanding of the theoretical and practical aspects
of Soft Computing.
Learning outcomes
After studying this course the student should be able to :
 Understand the need for Soft Computing;
 Understand different uses of Soft Computing in various areas;
 Understand the steps involved in the development of Soft Computing;
 Acquire a working knowledge of some popular tools for Soft
Computing;
 Design, implement and verify computing systems by using appropriate
Soft Computing techniques and tools.
Teaching methods
Lecture
Group discussion
Experiential exercise
Simulation
Case analysis
Course paper
Others
Methods
Midterm Exam
Case studies
Class Participation
Assignment and
quizzes
Project
Presentation/Group
Discussion
Final Exam
Others
Total
 Preparation for class
Evaluation
Policy
x
x
x
x
x
Date/deadlines
Percentage (%)
25
25
15
35
100
The structure of this course makes your individual study and preparation outside
the class extremely important. The lecture material will focus on the major points
introduced in the text. Reading the assigned chapters and having some familiarity
with them before class will greatly assist your understanding of the lecture. After
the lecture, you should study your notes and work relevant problems and cases
from the end of the chapter and sample exam questions.
Throughout the semester we will also have a large number of review sessions.
These review sessions will take place during the regularly scheduled class
periods.

Withdrawal (pass/fail)
This course strictly follows grading policy of the School of Engineering and
Applied Science. Thus, a student is normally expected to achieve a mark of at
least 60% to pass. In case of failure, he/she will be required to repeat the course
the following term or year.

Cheating/plagiarism
Cheating or other plagiarism during the Quizzes, Mid-term and Final
Examinations will lead to paper cancellation. In this case, the student will
automatically get zero (0), without any considerations.

Professional behavior guidelines
The students shall behave in the way to create favorable academic and
professional environment during the class hours. Unauthorized discussions and
unethical behavior are strictly prohibited.
We
ek
Tentative Schedule
Topics
Date/Day
(tentative)
1
11.02.13
Overview of course
18.02.13
Brief introduction to the platforms and required
background for the course.
Basic of Soft Computing
25.02.13
Introduction to Soft Computing.
The main components and characteristics of Soft
Computing.
Fuzzy Logic and Systems
2
3
Textbook/Assignments
Handout
Chapter 1 [1]
Handout
Chapter 4 [1]
Fuzzy Sets and Membership Functions.
Operations on Fuzzy Sets.
Fuzzification.
4
5
6
7
04.03.13
Fuzzy Numbers
11.03.13
Uncertain Fuzzy Values.
Fuzzy Numbers and its L-R representation.
Operations on Fuzzy Numbers.
Fuzzy Relations
18.03.13
Cartesian product.
Binary Fuzzy Relations. IF-THEN fuzzy relation.
n-ary Fuzzy Relations.
Compositions of Fuzzy Relations.
max-min composition.
max-product composition.
Fuzzy Inference Systems
Chapter 5 [1]
01.04.13
Architecture of Fuzzy Inference System.
Fuzzy Inference Rules and Reasoning.
Defuzzification.
Applications of Fuzzy Logic
Chapter 4 [1]
Handout
Web Resources
Chapter 4 [1]
Handout
Fuzzy Control Systems.
Pattern Analysis and Classification.
Fuzzy Expert Systems.
8
08.04.13
Midterm Exam
9
15.04.13
Neural Networks
Artificial Neural Networks.
Models of Neuron.
Architecture of Neural Networks.
Feed-forward Neural Networks.
Recurrent Neural Networks.
Network layers.
Perceptrons.
Chapter 6 [1]
10
11
22.04.13
Learning Methods for Neural Networks
29.04.13
Supervised Learning.
Unsupervised Learning.
Reinforcement Learning.
Transfer Function.
Back-Propagation Algorithm.
Applications of Neural Networks
Chapter 6 [1]
Handout
Chapter 7 [1]
Neural Networks in Business.
Neural networks in Medicine.
12
13
14
15
06.05.13
Genetic Algorithms and Evolutionary Computation.
Chapter 2 [1]
13.05.13
Basics of Genetic Algorithms :
Representation methods
Selection
Crossover
Mutation
Applications of Genetic Algorithms
Chapter 3 [1]
20.05.13
Genetic Algorithms on optimization and planning :
Traveling Salesman Problem.
Genetic Algorithms in Business and their role in
Decision Making.
Intelligent Control Using Evolutionary Computation.
Hybrid Systems
27.05.13
Fuzzy-Evolutionary System
Neuro-Fuzzy System
Hybrid Systems
Chapter 8,10 [1]
Chapter 9 [1]
Neuro-Fuzzy-Evolutionary System
Neuro-Evolutionary System
TBA
Final Exam
This syllabus is a guide for the course and any modifications to it will be announced in advance.