1. This document provides information about a course on special topics in electrical engineering. 2. It outlines the instructor's contact information, course details including credit hours and prerequisites, a description of course content covering fuzzy logic, neural networks, and optimization techniques, and intended learning outcomes. 3. The topics to be covered each week are listed, including basic concepts of fuzzy set theory, fuzzy logic controllers, neural network fundamentals, associative memory, and genetic algorithms. Assessment will be based on exams, coursework, and a final exam.

1. Course Specification Template
1. Generalinformation about Instructor:
Name Dr.Ja’far saifeddin Jallad Class Time & Office Hours
Phone Internal Day SUN MON TUE WED THU
External
Mobile Class Time 9.30-
11:00
9.30-
11.00
Instructor's
E-mail
j.jallad@ptuk.edu.ps Class Room H-102 H-102
Class Time
Class Room
Class Time
Class Room
Office
Hours
9-10 11:00-
12.30
9-10 9-10
2. Generalinformation about the Course
No Requirements
1 Course Title SpecialTopics
2 Course code & Number 12120527
3 Credit hours Theo. (CH): 3 Practical (CH): 0
4 Faculty Engineering and Technology
5 Department / Division that offers the
course:
Electrical Engineering Department
6 Course type Compulsory Elective
Uni. Fac. Dep. Uni. Fac. Dep.
7 Level and Semester Third year, first/second semester
8 Prerequisite(s) – If any Digital logic and digital Electronics
9 Co-requisite(s) – if any -----------
10 Program/programs for it/them the
course is offered
Electrical Engineering. Industrial Automation,
Mechatronics, Telecommunication
11 Instruction Medium: English Arabic
‫التقنية‬ ‫فلسطين‬ ‫جامعة‬–‫خضوري‬
‫والنوعية‬ ‫الجودة‬ ‫دائرة‬
‫طولكرم‬-‫ص.ب‬7
:‫هاتف‬2677923/09-2671026/09
:‫فاكس‬2677922/09
:‫إلكتروني‬ ‫بريد‬quality@ptuk.edu.ps
Palestine Technical University -Kadoorie
Quality Department
Tulkarm-P.O. Box: 7
Tel: 09/2761026 – 09/l2677923
Fax: 09/2677922
Email: quality@ptuk.edu.ps
X X
X

2. 3. Course description:
Background, Uncertainty and imprecision, Statistics and random processes,
Uncertainty in information, Fuzzy sets and membership, Chance versus ambiguity, Classical sets
operations on classical sets to functions, Fuzzy sets-fuzzy set operations, Properties of fuzzy sets.
Sets as points in hypercubes.
Optimization is the process of obtaining the best result under given circumstances. In
design, construction and maintenance of any engineering system, engineers have to take many
technological and managerial decisions at several stages. The ultimate goal of all such
decisions is either to minimize the effort required or to maximize the desired benefit. A
number of optimization methods have been developed for solving different types of optimization
problem
4. GeneralCourse Objectives
5. Intended Learning Outcomes/ILO’s (please specifythe learning outcomes ofthe
course as outlined below):
A) Knowledge and understanding
-To impact knowledge on fuzzy logic principles
- To understand models of ANN
-To use the fuzzy logic and neural network for application related to design and
manufacture
B) Intellectual/Cognitive skills
ability to apply knowledge of math engineering and science
ability to design and conduct experiments and ability to analyze and interpret data
ability to design system components or process to meet a need
ability to identify, formulate and solve engineering problems
C) Subject specialization and practical skills
 Develop the skill in basic understanding on fuzzy and neural network
 Explore the functional components of neural classification conducer and the
functional components of fuzzy logic classification on controller.
 Develop and implement a basic trainable neural network (or) a fuzzy logic system to
design and manufacturing.
D) General and transferable skills
ability to function in multidisciplinary teams
ability to use techniques, skills and tools in engineering practice
1.Introduce students to Fuzzy Logic.
2. Introduce students to ANN Models.
3. Explain the architecture of optimization techniques.
4. Explain different Applications of AI techniques in control system.

3. 6. Topics coveredand Calendar:
A. Theoretical parts (Please state the titles of the subjects you intend to cover each week)
7.
Student assessmentmethods basedon ILO,s
No Assessment method Week Mark Percentage to
overall mark
1. First Exam 30 30%
2. Second Exam 30 30%
3. Mid-term Exam (if any)
4. Coursework
5. Final Exam 40 40%
Number Topics Number of hours
1. Basic concepts of fuzzy set theory – operations of fuzzy sets
– properties of fuzzy sets – Crisp relations – Fuzzy relational
equations – operations on fuzzy relations – fuzzy systems –
propositional logic – Inference – Predicate Logic – Inference
in predicate logic – fuzzy logic principles – fuzzy quantifiers
– fuzzy inference – fuzzy rule based systems – fuzzification
and defuzzification – types.
9
2. Fuzzy logic controllers – principles – review of control
systems theory – various industrial applications of FLC
adaptive fuzzy systems – fuzzy decision making –
Multiobjective decision making – fuzzy classification – means
clustering – fuzzy pattern recognition – image processing
applications – systactic recognition – fuzzy optimization.
9
3. Fundamentals of neural networks – model of an artificial
neuron – neural network architectures – Learning methods –
Taxonomy of Neural network architectures – Standard back
propagation algorithms – selection of various parameters –
variations Applications of back propagation algorithms.
9
4. Associative memory – exponential BAM – Associative
memory for real coded pattern pairs – Applications adaptive
reasonance theory – introduction – ART 1 – ART2 –
Applications – neural networks based on competition –
kohenen self organizing maps – learning vector quantization
– counter propagation networks – industrial applications.
9
5. Fundamentals of genetic algorithms – genetic modeling –
hybrid systems – integration of fuzzy logic, neural networks
and genetic algorithms – non traditional optimization
techniques like ant colony optimization – Particle swarm
optimization and artificial immune systems – applications in
design and manufacturing.
9
6.
7.
8.
9.

4. 8. Referencesand other resources
A. Recommended Textbook(s): two maximum
1. Rajasekaran. S.. Vijayalakshmi Pai. G.A. “Neural Networks, Fuzzy Logic and Genetic
Algorithms”, Prentice Hall of India Private Limited, 2003
2. Timothy J.Ross, “Fuzzy logic with Engineering Applications”, McGraw Hill, 1995
3.
B. Other references
1. Zurada J.M. “Introduction to Artificial Neural Systems”, Jaico publishing
house, 1994.
2. Gen, M. and Cheng R. “Genetic Algorithm and Engineering Design”, john wiley
1997
3.
C. Electronic resources, Websites related to the course
1.
2.
Name & signature of Head of department/ program leader
Name: …………………………… signature: …………………………Date: ……………….
Name & signature of Quality rep. in your faculty
Name: …………………………… signature: …………………………Date: ……………….
Course Tutor’s name and signature
Name: Basim Alsayid ………… signature: …………………………Date: ……………….