Course detail
Fuzzy Systems
FEKT-MPC-FSYAcad. year: 2023/2024
Motivation, crisp sets and fuzzy sets. Fuzzy sets operations, t-norms and conorms. Fuzzy relations and operations with them. Projection, cylindrical extension, composition. Approximate reasoning. Linguistic variable. Fuzzy implication. Generalized modus ponens and fuzzy rule if-then. 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.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
Project- 20 points.
Final written test- 65 points.
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.
Aims
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
Study aids
Prerequisites and corequisites
Basic literature
JURA,P. Slajdy přednášek předmětu MFSY (CS)
Recommended reading
Elearning
Classification of course in study plans
- Programme MPC-KAM Master's 1 year of study, summer semester, compulsory-optional
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Operation with the fuzzy sets.
t-norm a conorm.
Fuzzy relation and operations with them. Projection, cylindrical extension, composition.
Approximate reasoning. Linguistic variable. Fuzzy implication.
Generalised modus ponens, fuzzy rule if-then. 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.
Neuro-fuzzy systems.
Exercise in computer lab
Teacher / Lecturer
Syllabus
Demonstration examples in Fuzzy toolbox Matlab.
Individual solving of a simple task. Project definition a its individual solving.
Project
Teacher / Lecturer
Syllabus
Elearning