Course Title:
Introduction to Fuzzy
Informatics and Intelligent Systems
Credits: 3
Prerequisite(s): EE32210
Signals and Systems (or
EE 31113 Systems and Control) and
MA2149
Mathematical
Analysis (or Equivalent)
Precursor(s): nil
Equivalent Courses: nil
Total hours: Lectures: 38 hours
Labs: 4 hours
1. Aims and Objectives
1.1 Aim
This course presents some fundamental knowledge of fuzzy sets, fuzzy logic, fuzzy decision
making and fuzzy control systems. The aim is to equip graduate students with some state-of-the-art
fuzzy-logic technology and fuzzy system design methodologies, thereby better preparing them for
the rapidly evolving high-tech information-based financial market and modern industry.
1.2 Objectives
[1] Be able to understand basic knowledge of fuzzy sets and fuzzy logic
[2] Be able to apply basic knowledge of fuzzy information representation and processing
[3] Be able to apply basic fuzzy inference and approximate reasoning
[4] Be able to understand the basic notion of fuzzy rule base
[5] Be able to apply basic fuzzy system modelling methods
[6] Be able to apply basic fuzzy PID control systems
[7] Be able to understand the basic notion of computational verb controllers
2. Syllabus
2.1.
Introduction to fuzzy sets
The uncertain and inexact nature of the real world: ideas and examples;
fuzzy membership functions; fuzzy numbers and fuzzy arithmetic
2.2
Introduction to fuzzy logic
Basic concept and properties of fuzzy logic versus classical two-valued
logic
2.3
Introduction to fuzzy inference
Fuzzy inference principles; fuzzy decision making; approximate
reasoning
2.4
Introduction to fuzzy rule base
If-Then rules; general format of fuzzy rule base; establishment
of fuzzy rule base
2.5
Introduction
to intelligent decision-making
Multi-objective optimization, performance evaluation,
decision-making
2.6
Introduction to fuzzy modeling
Static fuzzy modeling; dynamic fuzzy modeling
2.7
Introduction to fuzzy control systems
Basic fuzzy control principle: example of set-point tracking; open-loop
and
closed-loop fuzzy control systems; fuzzy PID controllers design methods
and
applications, and
computational verb controllers
3. Teaching Methods
Lecturing is the core of teaching. Substantial reading material will be assigned, followed by students' practice.
Two computer projects on fuzzy decision making and tracking control will be given for which simulation
reports are required. One set of homework is due every another week.
4. Assessment
Homework: 20%
Projects:
20%
Examination: 60%
(one 2-hour final exam)
5. Textbook:
Prof.
G. Chen's
Class Notes (electronic
version, available for download)
Reference: G.
Chen and T. T. Pham,
Introduction to Fuzzy Systems,
CRC Press, 2006
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