PDF Course Plan - zu.edu.jo

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ZU/QP10F004

Faculty: Information Technology

Program: Bachelor’s degree Department: Data Science

and AI

Semester:

Academic year:

Course Plan

First: Course Information

Credit Hours: 3 Course Title:

Fuzzy Logic

Course No.

1505303

Lecture Time:

Section No.: 1 Prerequisite:

1501220

Obligatory Faculty Requirement Elective University Requirement Obligatory University Requirement Faculty Requirement

Course Elective Specialty Requirement Obligatory Specialization requiremen

t

Type Of Course:

Face-to-Face Learning

Blended Learning(2 Face-to-Face + 1Asynchronous) Online Learning (2 Synchronous+1 Asynchronous) Type of

Learning:

Second: Instructor’s Information

Academic Rank:

Name:

E-mail:

Ext. Number:

Office Number:

Sunday Monday Tuesday Wednesday Thursday Office Hours:

Third: Course Description

Fuzzy logic is a design method that can be effectively applied to problems that, because of complex, nonlinear or ambiguous system models, cannot be easily solved using traditional analytical control techniques. This course discusses the types of applications for which fuzzy control is useful and introduces basic concepts of fuzzy set theory, fuzzy logic operations, fuzzification and

defuzzification. Several types of fuzzy control design.

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Fourth: Learning Source

Fuzzy logic with engineering applications Main Reference:

Publication Year: 2010 Issue No.: 3

^rd

Author: Timothy J.Ross

 Moodle

 George J.KlirBo Yuan - Fuzzy sets and Fuzzy logic theory and Applications, PHI, New Delhi,1995

 S.Rajasekaran, G.A.Vijayalakshmi - Neural Networks and Fuzzy logic and Genetic Algorithms, Synthesis and Applications, PHI, New

Delhi,2003

 http://www.nptel.ac.in/syllabus/syllabus.php?subjectId=111106048 Additional

Sources&Websites:

Classroom Laboratory Workshop MS Teams Moodle Teaching Type:

Fifth: Learning Outcomes

Connection To Program ILOs

Code Course Intended Learning Outcomes (CILOs)

Course Code

Knowledge

*PK1 Foster competence in recognizing the feasibility and applicability

of the design and implementation of intelligent systems (that employ fuzzy logic) for specific application areas.

**K1

Help students develop a sufficient understanding of fuzzy system PK1 design methodology and how it impacts system design and performance.

K2

Skills

PS2, PS4 Understand the basic ideas of fuzzy sets, operations and properties

of fuzzy sets and also about fuzzy relations.

***S1

PS2, PS4 Design fuzzy rule based system.

S2

Competencies

PC4 Communication and collaboration

****C1

PC4 Teamwork

C2

PC3 Creativity

C3

PC4 Leadership

C4

PC2 Critical thinking

C5

* P: Program, K: knowledge, *S: skills, ****C: competencies.

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Sixth: Course Structure

Lecture Date

Intended Teaching Outcomes(ILOs)

Topics Teaching Procedures*

Teaching Methods**

References**

*

Week 1

C1, C2,C3, C4 , C5 Course Syllabus

discussion Face to Face Lecture -

K1

Background, Uncertainty and Imprecision, Statistics and Random Processes

Face to Face

Lecture , quizzes and assignments

Slides on the Moodle

K1

General Questions about Statistics and Random Processes, Uncertainty in Information, Fuzzy Sets and Membership, Chance versus Ambiguity

Model /Asynchronous 1

Assignment Slides on the Moodle

Week 2

K1, K2

Uncertainty in Information, Fuzzy Sets and Membership, Chance versus Ambiguity

Face to Face

K1, K2, S1

Classical Sets -

Operations on Classical Sets, Properties of Classical (Crisp) Sets

Face to Face

K1, K2, S1

Classical Sets -

Operations on Classical Sets, Properties of Classical (Crisp) Sets, Mapping of Classical Sets to Functions Fuzzy Sets - Fuzzy Set operations, Properties of Fuzzy Sets

video Slides on the Moodle

Week 3

K1, K2, S1

Mapping of Classical Sets to Functions Fuzzy Sets - Fuzzy Set operations, Properties of Fuzzy Sets

Face to Face

K1, K2, S1

Cartesian Product, Crisp Relations- Cardinality of Crisp Relations, Operations on Crisp Relations, Properties of Crisp Relations,

Composition.

Face to Face

K1, K2, S1

Introduction to Cartesian Product, Crisp Relations- Cardinality of Crisp Relations, Operations on Crisp Relations, Properties of Crisp Relations

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Week 4

K1, K2, S1, S2

Fuzzy Relations - Cardinality of Fuzzy Relations, Operations on Fuzzy Relations, Properties of Fuzzy Relations, Fuzzy Cartesian Product and Composition, Non- interactive Fuzzy Sets

Face to Face

K1, K2, S1, S2

Tolerance and

Equivalence Relations - Crisp Equivalence Relation, Crisp Tolerance Relation, Fuzzy Tolerance and Equivalence Relations

Face to Face

K1, K2, S1, S2

Fuzzy Relations - Cardinality of Fuzzy Relations, Operations on Fuzzy Relations, Properties of Fuzzy Relations, Fuzzy Cartesian Product and Composition, Non- interactive Fuzzy Sets

Week 5

K1, K2, S1, S2

Value Assignments - Cosine Amplitude, Max-min Method, Other Similarity methods

Face to Face

K1, K2, S1, S2

Features of the Membership Function, Standard Forms and Boundaries, Fuzzification

Face to Face

K1, K2, S1, S2

Value Assignments - Cosine Amplitude, Max-min Method, Other Similarity methods.

Model

/Asynchronous 5 video Slides on the Moodle

Week 6

K1, K2, S1, S2

Membership Value Assignments – Intuition, Inference, Rank Ordering, Angular Fuzzy Sets, Neural Networks, Genetic Algorithms, Inductive Reasoning

Face to Face

K1, K2, S1, S2, C1

Lambda-Cuts for Fuzzy Sets, Lambda- Cuts for Fuzzy Relations, Defuzzification Methods Extension Principle - Crisp Functions

Face to Face

K1, K2, S1, S2, C1 Membership Functions Model

/Asynchronous6 video Slides on the Moodle

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Week 7

K1, K2, S1, S2, C1

Mapping and

Relations, Functions of fuzzy Sets – Extension Principle, Fuzzy Transform (Mapping), Practical

Considerations

Face to Face

K1, K2, S1, S2, C1

Fuzzy Numbers Interval Analysis in Arithmetic,

Approximate Methods of Extension - Vertex method

Face to Face

K1, K2, S1, S2, C1

Fuzzy-to-Crisp Conversions, Fuzzy Arithmetic

Model

/Asynchronous 7 Assignment Slides on the Moodle

Week 8

K1, K2, S1, S2, C1

DSW Algorithm, Restricted DSW Algorithm,

Comparisons, Fuzzy Vectors

Face to Face

K1, K2, S1, S2, C1

Classical Predicate Logic – Tautologies, Contradictions, Equivalence, Exclusive OR and Exclusive NOR, Logical Proofs, Deductive Inferences

Face to Face

K1, K2, S1, S2, C1

Examples that compare between Classical Logic and Fuzzy Logic

Model

/Asynchronous 8 video Slides on the Moodle

Week 9

K1, K2, S1, S2, C1

Fuzzy Logic, Approximate Reasoning, Fuzzy Tautologies, Contradictions, Equivalence and Logical Proofs

Face to Face

K1, K2, S1, S2, C1

Natural Language, Linguistic Hedges, Rule-Based Systems - Canonical Rule Forms, Decomposition of Compound Rules

Face to Face

K1, K2, S1, S2, C1

Natural Language, Linguistic Hedges, Rule-Based Systems - Canonical Rule Forms, Decomposition of Compound Rules

Week 10 K1, K2, S1, S2, C1

Likelihood and Truth Qualification, Aggregation of Fuzzy Rules, Graphical Techniques of Inference

Face to Face

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K1, K2, S1, S2, C1

Fuzzy Synthetic Evaluation, Fuzzy Ordering

Face to Face

K1, K2, S1, S2, C1 Revision and discussion

Online meeting using Teams.

Week 11

K1, K2, S1, S2, C1

Preference and consensus, Multiobjective Decision Making

Face to Face

K1, K2, S1, S2, C1, C2

Fuzzy Bayesian Decision Method, Decision Making under Fuzzy States and Fuzzy Actions

Face to Face

K1, K2, S1, S2, C1, C2

Fuzzy Synthetic Evaluation, Fuzzy Ordering, Preference and consensus, Multi objective Decision Making.

Assignment Slides on the Moodle

Week 12

K1, K2, S1, S2, C1, C2

Classification by Equivalence Relations - Crisp Relations

Face to Face

S1, S2, C2 Fuzzy Relations.

Cluster Analysis Face to Face

S1, S2, C2

Fuzzy Bayesian Decision Method, Decision Making under Fuzzy States and Fuzzy Actions

Week 13

S1, S2, C2

Cluster Validity, c- Means Clustering - Hard c-Means (HCM)

Face to Face

S1, S2,C1, C2 Classification Metric Face to Face

S1, S2,C1, C2 Fuzzy Classification techniques

Week 14

S1, S2,C1, C2 Hardening the Fuzzy

c-Partition Face to Face

S1, S2,C1, C2 Similarity Relations

from Clustering Face to Face

S1, S2,C1, C2 Fuzzy Logic Applications

Online discussion activity

* Learning procedures: (Face-to-Face, synchronous, and asynchronous). * * Teaching methods: (Lecture, video…..). **

* Reference: (Pages of the book, recorded lecture, video….).

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Seventh: Assessment methods

Methods

Fully Electronic Education

Blended Learning

Face-To-Face Learning

Measurable Course (ILOs)

First Exam Second Exam

Mid-term Exam 0 0 35 K1, K2, K3, S1, K1,

K2, K3, S1, S2, C1

Participation 0 0 15 K1, K2, K3, S1, S2, S3,

C1, C2

Asynchronous Meetings

Final Exam 0 0 50 K1, K2, K3, S1, S2, S3,

C1, C2, C3, C4, C5

Eighth: Course Policies

 All course policies are applied on all teaching patterns (online, blended, and face- to-face Learning) as follows:

a. Punctuality.

b. Participation and interaction.

c. Attendance and exams.

 Academic integrity: (cheating and plagiarism are prohibited).

PDF Course Plan - zu.edu.jo

Issue Date:11/7/2021 issue:02

Course Elective Specialty Requirement Obligatory Specialization requiremen

defuzzification. Several types of fuzzy control design.

 S.Rajasekaran, G.A.Vijayalakshmi - Neural Networks and Fuzzy logic and Genetic Algorithms, Synthesis and Applications, PHI, New

Knowledge

PS2, PS4 Understand the basic ideas of fuzzy sets, operations and properties

* P: Program, **K: knowledge, ***S: skills, ****C: competencies.

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a. Punctuality.

* P: Program, K: knowledge, *S: skills, ****C: competencies.