Course Website 

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