Non-Conventional Methods of Mechatronical Systems Control
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | English |
| Code | 450-8707/02 |
| Abbreviation | NMŘMS |
| Course title | Non-Conventional Methods of Mechatronical Systems Control |
| Credits | 4 |
| Coordinating department | Department of Cybernetics and Biomedical Engineering |
| Course coordinator | doc. Ing. Zdeněk Slanina, Ph.D. |
Subject syllabus
1. Artificial intelligence principles, non-conventional description of complex systems
2. Principles and approaches of knowledge based non-numerical modelling and control
3. Principles of fuzzy sets and fuzzy linguistic logic
4. Rule based modelling, aproximative reasoning and results interpretation, expert systems
5. Fuzzy logic based controller analysis and synthesis, structures and their properties in comparison with conventional controllers
6. Fuzzy control and its application in mechatronics
7. Artificial neural networks, structures and self-learning principles
8. Neural controller synthesis an their properties discussion
9. Combined fuzzy-neural controllers
10. Evolution and genetic algorithms in tasks of optimization of structures and parameters of conventional controllers
11. Genetic algorithms application issues, advanced genetic algorithms and their properties
12. Structural and parameter optimization of non-conventional controllers using genetic algorithms
13. Computational intelligence, integrated fuzzy-neuro-genetic structures in control
14. Intelligent controllers, their structure and application
2. Principles and approaches of knowledge based non-numerical modelling and control
3. Principles of fuzzy sets and fuzzy linguistic logic
4. Rule based modelling, aproximative reasoning and results interpretation, expert systems
5. Fuzzy logic based controller analysis and synthesis, structures and their properties in comparison with conventional controllers
6. Fuzzy control and its application in mechatronics
7. Artificial neural networks, structures and self-learning principles
8. Neural controller synthesis an their properties discussion
9. Combined fuzzy-neural controllers
10. Evolution and genetic algorithms in tasks of optimization of structures and parameters of conventional controllers
11. Genetic algorithms application issues, advanced genetic algorithms and their properties
12. Structural and parameter optimization of non-conventional controllers using genetic algorithms
13. Computational intelligence, integrated fuzzy-neuro-genetic structures in control
14. Intelligent controllers, their structure and application
E-learning
Materials are available at https://lms.vsb.cz/?lang=en.