Title:  Fuzzy Systems for Control and Modelling 

Code:  FSY 

Ac.Year:  2009/2010 

Term:  Summer 

Study plans:  

Language:  Czech 

Credits:  5 

Completion:  accreditation+exam (written) 

Type of instruction:  Hour/sem  Lectures  Sem. Exercises  Lab. exercises  Comp. exercises  Other 

Hours:  26  0  0  0  26 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  55  15  0  0  30 



Guarantee:  Jura Pavel, prof. Ing., CSc., DAME 

Faculty:  Faculty of Electrical Engineering and Communication BUT 

Department:  Department of Control and Instrumentation FEEC BUT 


Learning objectives: 

The goal of the course is to acquaint with the fundamentals of fuzzy sets theory and fuzzy logic. Students learn to apply the fuzzy theory for modelling of te uncertainty systems. They acquaint with adaptive techniques in the fuzzy systems. 
Description: 

Motivation, crisp sets and fuzzy sets. Fuzzy sets operations, tnorms and conorms. Fuzzy relations and operations with them. Projection, cylindrical extension, composition. Approximate reasoning. Linguistic variable. Fuzzy implication. Generalized modus ponens and fuzzy rule "ifthen". Inference rules. The evaluation of a set of the fuzzy rules. Fuzzy systems Mamdani and Sugeno. The structure of the system, knowledge and data base. Fuzzification and defuzzification. Fuzzy system as an universal approximator. Adaptive fuzzy systems, neuro fuzzy systems. 
Learning outcomes and competences: 

The student has fundamental knowledge and skill in the fuzzy theory. He knows to apply it in the field of the modelling and control of the uncertainty defined systems. 
Syllabus of lectures: 

 Motivation, crisp sets and fuzzy sets.
 Operation with the fuzzy sets.
 tnorm a conorm.
 Fuzzy relation and operations with them. Projection, cylindrical extension, composition.
 Approximate reasoning. Linguistic variable. Fuzzy implication.
 Generalised "modus ponens", fuzzy rule "ifthen". Inference rules.
 Evaluation of the set of fuzzy rules.
 Fuzzy systems Mamdani a Sugeno.
 The structure of the fuzzy system, knowledge and data base.
 Fuzzification and defuzzification.
 Fuzzy system is an universal approximator.
 Adaptive fuzzy systems.
 Neurofuzzy systems.

Syllabus  others, projects and individual work of students: 

Mamdani or Sugeno type model in one implemented example. 
Fundamental literature: 

 Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Logic, SpringerVerlag, 1993 ISBN 3540563628.

Study literature: 

 Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Logic, Supported book, SpringerVerlag, 1993, ISBN 8021422610.

Progress assessment: 

One midsemestr written test. 
Exam prerequisites: 

Working out of the project. 
