• Motivation
  This course is an introduction to fuzzy logic, fuzzy set theory and neural networks. Fuzzy logic is a tool that can be applied to ambiguous, complicated, complex, or nonlinear systems or problems, which cannot easily solved by classical techniques. Fuzzy logic is basically a multivalued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false, black/white, etc. Notions like rather warm or pretty cold can be formulated mathematically and processed by computers. In this way an attempt is made to apply a more human-like way of thinking in the programming of computers.
  

• Course Outline
  The following topics are covered in the course:

  1. Introduction: background and applications.
  2. Crisp logic: An introduction.
  3. Fuzzy Mathematics: Fuzzy Sets, Fuzzy membership functions, Operators and relations, Linguistic variables and hedges.
  4. Fuzzy Resoning: Fuzzy If-then rules, Reasoning and inference systems, Approximate resoning, Fuzzy rules.
  5. Fuzzy control: Fuzzy control systems.
  6. Neural Networks: An introduction, ANFIS.

• Textbooks and References
  1. B. Kosko, Neural Networks and Fuzzy Systems, Prentice-Hall International Inc., 1992.
  2. J. R. Jang, C. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall International Inc., 1997.

• course E-Mail: kntu.fuzzy@gmail.com
  

• Assignments

  Miscellaneous
  ● Inverted Pendulum                  
  ● Fuzzy Toolbox User's Guide   

   

  ● 1st Assignment     
  ● 2nd Assignment     
  ● 3rd Assignment     
  
  

• Presentations
  ● Introduction to Optimization     
  ● 2nd presentation                         
  ● 3rd presentation                         

•  

  • Projects
    ● 1st Project      
    ● 2nd Project    
    

   

Last updated: October 14, 2010| © 2010 Ali Keymasi
KNTU|Pardis Alley|Sadra Street|Vanak Square|Tehran |Iran