638-3010/03 – Methods of Artificial Intelligence (MUI)
Gurantor department | Department of Automation and Computing in Industry | Credits | 6 |
Subject guarantor | doc. Ing. Jiří David, Ph.D. | Subject version guarantor | doc. Ing. Jiří David, Ph.D. |
Study level | undergraduate or graduate | Requirement | Compulsory |
Year | 2 | Semester | winter |
| | Study language | Czech |
Year of introduction | 2019/2020 | Year of cancellation | 2021/2022 |
Intended for the faculties | FMT | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Student will be able to formulate and suggest the solution with utilization the instruments artificial inteligence.
Student will be able to create the knowledge base for the intelligent controller.
Student will be able to analyze the problems technical application in field the engineering cybernetics with utilization the instruments artificial inteligence.
Student will be able to formulate and suggest the solution problems with utilization Matlab toolboxs Fuzzy Logic, Neural Network and Genetic Algorithm.
Teaching methods
Lectures
Tutorials
Project work
Summary
Subjekt put mind to the questions artificial inteligence. Students do one's homework the control models on platform the artificial inteligence (qualitative and semi-qualitative models, fuzzy models, the knowledge-based systém,systems of neural network and genetic algorithm).
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Písemný test a ústní zkoušení.
E-learning
Other requirements
Getting to know the practical solution of problems with the use of artificial intelligence methods.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Control as a informative incidence, activity and process.
2. Determination theory modelling and artificial intelligence.
3. Qualitative models.
4. Semiqualitative models.
5. Fuzzy model, introduction to fuzzy models.
6. Fuzzy multiple theory.
7. More value logic and language model.
8. Fuzzy systems
9.Strategy fuzzification, strategy defuzzification.
10. Expert systems, definition and architecture expert systems.
11. Knowledge base, inferential mechanism, interpretation answer expert systems.
12. Expert systems and control.
13. Introduction to the neuronal systems.
14. Neuronal networks.
15. Genetic algorithms.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction