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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