| Course Unit Code | 450-4017/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit |
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| Name of Lecturer(s) | Personal ID | Name |
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| SLA77 | Ing. Zdeněk Slanina, Ph.D. |
| Summary |
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The course is focused on the area of modern artificial intelligence methods application in the information and control systems. Introduces the complex systems modelling problems and linguistic describing of its behaviour using the human knowledge. Basic procedures of fuzzy mathematics and fuzzy logic are presented including the fuzzy control systems development methodology.
The subject appears to be introduction to artificial intelligence methodologies and their application inengineering.. |
| Learning Outcomes of the Course Unit |
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The course is aimed on fuzzy set theory, fuzzy logic, fuzzy control theory and their application in development of knowledge based fuzzy controllers.
After passing the course students have the main knowledge and skills with development, debugging and testing of fuzzy controllers in engineering. |
| Course Contents |
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Lectures
1. Artificial intelligence, knowledge importance, vagueness and its formalization, knowledge properties, knowledge processing, knowledge systems
2. Principles of fuzzy set theory, fuzzy sets operations, Zadeh´s extesional principle
3. Linguistic variable and its fuzzy sets reperesentation
4. Principles of fuzzy logic, fuzzy-logical operations
5. Linguistic model aproximation using fuzzy function, conditional IF-THEN rules
6. Principles of fuzzy modelling, Mamdani fuzzy models
7. Inference engines, Zadeh´s extrapolation principle
8. Takagi-Sugeno fuzzy models and their properties
9. Basic structure and paramemers of fuzzy controllers FLC, types of fuzzy controllers
10. Types of fuzzy controllers and their control rules
11. Operations of fuzzification and deffuzification
12. Fuzzy control systems stability investigation
Fuzzy controllers design
14. Fuzzy state controllers
Laboratory works
1. Fuzzy control using microprocessor NXP iMX6
2. Fuzzy control using PLC Siemens
Computer works
1. Principles of programme system Matlab, FuzzTool Box (FTB), programme tool Simulink
2. Fuzzy sets - editing, parameters of fuzzy sets approximation, fuzzy sets operations (FTB), examples - Zadeh´s extesional principle
3. Linguistic values of linguistic variables editing (FTB), teaching programme Fuzzy Logic Motorola
4. Effect of type of fuzzy logic function on results of fuzzy sets operations (FTB)
5. Example of linguistic model approximation using a fuzzy function (FTB)
6. Mamdani models editing (FTB)
7. Examples of Zadeh´s intrpolation principle utilization (FTB)
8. Takagi-Sugeno model editing, non-linear function approximation (FTB)
9. Takagi-Sugeno model identification using ANFIS procedure (FTB)
10. Fuzzy controller Mamdani, teaching programme FL Mortorola
11. Fuzzy controller Mamdani, design and debugging
12. Fuzzy controller Takagi-Sugeno, design and debugging
Semestral project
Design a fuzzy controller Mamdani with sheduled system using programme environment Matlab and Simulink ! |
| Recommended or Required Reading |
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| Required Reading: |
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GANG,F. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. CRC Press, 2010. ISBN 9781420092646
RUSSEL,S., NORVIG,P.: Artificial Intelligence, Prentice-Hall, Inc., 2003, ISBN 0-13-080302-2
MATA,F., MARICHAL,G.N., JIMNEZ,E. Fuzzy Modeling and Control: Theory and Applications. Atlantis Publishing Corporation, 2014 ISBN 9462390819
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POKORNÝ,M.,SROVNAL,V. Znalostní systémy řízení – Učební text a návody do cvičení. CZ.1.07/2.2.00/15.0113. VŠB - Technická univerzita Ostrava. Ostrava. 2012
JURA, P. Základy fuzzy logiky pro řízení a modelování. Brno: Nakladatelství VUTIUM, 2003, ISBN 80-214-2261-0.
VOLNÁ,E. Neuronové sítě. Ostravská univerzita, Ostrava. 2008
HYNEK,J. Genetické algoritmy a genetické programování. Grada, 2008. ISBN: 978-80-247-2695-3
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| Recommended Reading: |
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C. R. REEVES, J. E. ROW,. Genetic Algorithms: Principles and Perspectives. Kluwer Academic Publishers, New York, 2002.
GRAUPE,D. Principles of Artificial Neural Netvorks. World Scientific. 2013. ISBN: 978-981-4522-73-1
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VESELÝ,A. Úvod do umělé inteligence. ČZU Praha, 2005. ISBN 80-213-1361-7
NOVÁK,V.,KNYBEL,J. Fuzzy modelování. Ostravská univerzita v Ostravě, Přírodovědecká fakulta. Ostrava 2005.
MATA,F., MARICHAL,G.N., JIMNEZ,E. Fuzzy Modeling and Control: Theory and Applications. Atlantis Publishing Corporation, 2014 ISBN 9462390819
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| Planned learning activities and teaching methods |
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| Lectures, Individual consultations, Tutorials, Experimental work in labs, Project work |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 40 | 21 |
| Examination | Examination | 60 (60) | 30 |
| Písemná zkouška | Written examination | 40 | 10 |
| Ústní zkouška | Oral examination | 20 | 6 |