prof. Ing. Pavel Jura, CSc.

Department of Control and Instrumentation (UAMT)

An absolvent is able to:
- explain the difference between classical and fuzzy set
- explain the notion linguistic variable
- apply the operation with fuzzy sets to mathematical description of approximate reasoning
- name and explain attributes of set of fuzzy rules
- name and explain two types of fuzzy systems
- explain the function of fuzzy system as a universal approximator
- describe of adaptation in the fuzzy systems

The basic knowledge of set theory and logic, basic knowledge of system theory and control theory (on the level of bachelor's study)

Not applicable.

Not applicable.

JURA, P.; Základy fuzzy logiky pro řízení a modelování, Brno VUTIUM, 2003, 132 s. ISBN 80-214-2261-0. (CS)
DRIANKOV, D.; HELLENDOORN, H.; REINFRANK, M. An Introduction to Fuzzy Logic. Springer-Verlag, 1993. 316 p. ISBN 3-540-56362-8. (EN)
KLIR, G.J.; BO YUAN. Fuzzy Sets and Fuzzy Logic. Theory and Applications, Prentice Hall PTR, 1995. 574 p. ISBN 0-13-101171-5. (EN)
ZADEH, L.A. Fuzzy sets. Information and Control 8, pp. 338-353, (1965) (CS)
ZADEH, L.A.. From Computing with Numbers to Computing with Words—From Manipulation of Measurements to Manipulation of Perceptions. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 45, NO. 1, JANUARY 1999, pp. 105-119. (CS)
ZADEH, L.A. Fuzzy logic—a personal perspective. Fuzzy Sets and Systems 281 (2015) 4-20, pp.4-20. (CS)
SUBBULAKSHMI, K. Antilock-Braking System Using Fuzzy Logic. Middle-East Journal of Scientific Research 20 (10) 2014, pp. 1306-1310. ISSN 1990-9233 (CS)
JURA,P. Slajdy přednášek předmětu MFSY (CS)

Teachning methods include lectures and computer laboratories. Students have to write a single project/assignment during the course.

Written test- 15 points during semester.
Project- 20 points.
Final written test- 65 points.
The exam from the subject will take place remotely.

Czech

Not applicable.

1. Motivation, crisp sets and fuzzy sets.
2. Operation with the fuzzy sets.
3. t-norm a t-conorm.
4. Fuzzy relation and operations with them. Projection, cylindrical extension, composition.
5. Approximate reasoning. Linguistic variable. Fuzzy implication.
6. Generalised modus ponens, fuzzy rule if-then. Inference rules.
7. Evaluation of the set of fuzzy rules.
8. Fuzzy systems Mamdani a Sugeno.
9. The structure of the fuzzy system, knowledge and data base.
10. Fuzzification and defuzzification.
11. Fuzzy system is an universal approximator.
12. Adaptive fuzzy systems.
13. Neuro-fuzzy systems.

The goal of the subject is to acquaint with the fundamentals of fuzzy sets theory and fuzzy logic. Students learn to apply the fuzzy theory at modelling of the uncertainty systems. They acquaint with adaptive techniques in the fuzzy systems.

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

• Programme MPC-KAM Master's, 1. year of study, summer semester, 6 credits, compulsory-optional
  

13 hours, compulsory

prof. Ing. Pavel Jura, CSc.

eLearning: currently opened course