Course: Applications of Neural and Fuzzy Systems

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Course title Applications of Neural and Fuzzy Systems
Course code KEI/ANF
Organizational form of instruction Lecture + Tutorial
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech, English
Status of course Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Weissar Petr, Ing. Ph.D.
Course content
Biological neural networks, brain. Model of neuron. Hopfield network. Hamming network. Perceptron. Multilayer perceptron. Kohonnen selforganizing maps. Fuzzy sets, using of controllers. Fuzzyfication and defuzzyfication. Special sorts od neural networks for specified applications. Practical realisation of fuzzy methods. Application of concrete methods in practical use.

Learning activities and teaching methods
Task-based study method, Individual study, Lecture, Practicum
  • Presentation preparation (report) (1-10) - 10 hours per semester
  • Preparation for an examination (30-60) - 40 hours per semester
  • Preparation for comprehensive test (10-40) - 30 hours per semester
  • Contact hours - 26 hours per semester
  • Practical training (number of hours) - 26 hours per semester
prerequisite
Knowledge
using the Matlab program on the basic application level
Skills
the knowledge of the use of graphical output in the Matlab environment
learning outcomes
Knowledge
identify a basic patterns of biological activity of neural networks, including the most complex system represented by the human brain
compare the different types of neural networks
Skills
apply knowledge of specialized NeuralNet toolbox in Matlab for solving neural networks
design and verify the parametrs of neural network tuning parameters in particular for an optimal learning
suggest the appropriate type of neural network for solving a specific task
estimate the intensity of the exercise of the algorithms for setting the parameters of learning and performance of the computer system used
design and verify process parameters and fuzzyfication and defuzzyfication the selected type of fuzzy controller
Competences
N/A
teaching methods
Knowledge
Lecture
Practicum
Task-based study method
Individual study
Skills
Practicum
Laboratory work
Competences
Practicum
assessment methods
Knowledge
Combined exam
Test
Skills
Seminar work
Competences
Individual presentation at a seminar
Recommended literature
  • Weissar. Texty v síti.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester