| Course Unit Code | 638-3002/03 |
|---|
| Number of ECTS Credits Allocated | 5 ECTS credits |
|---|
| Type of Course Unit * | Compulsory |
|---|
| Level of Course Unit * | Second Cycle |
|---|
| Year of Study * | First Year |
|---|
| Semester when the Course Unit is delivered | Winter Semester |
|---|
| Mode of Delivery | Face-to-face |
|---|
| Language of Instruction | Czech |
|---|
| Prerequisites and Co-Requisites | |
|---|
| Prerequisities | Course Unit Code | Course Unit Title |
|---|
| 638-2008 | System Theory |
| 638-2012 | System Identification |
| Name of Lecturer(s) | Personal ID | Name |
|---|
| JAN45 | prof. Ing. Zora Koštialová Jančíková, CSc. |
| ZIM018 | Ing. Ondřej Zimný, Ph.D. |
| Summary |
|---|
The aim of the course is to acquaint with the methods of implementation of simulation models of dynamic systems. The explanation is based on the mathematical description of the dynamic system. Students are explained the principles of mathematical and physical modelling, principles of theory of similarity and modelling and to the methods necessary for implementation of the model on a digital computer. Students are introduced to artificial intelligence (fuzzy models, expert models, models of neural networks, genetic algorithms), attention is paid mainly to the models of neural networks and their application to the selected technological processes.
The exercises consist of creation of mathematical models for selected real dynamic systems and their verification using SIMULINK simulation program. Models of real processes with the use of artificial neural networks are created using software Statistica - Neural Networks, MATLAB -Neural Network Toolbox and NEUREX.
|
| Learning Outcomes of the Course Unit |
|---|
Student will be able to formulate the basic methods of simulation models realization on the digital computer.
Student will get an overview of the basic principles of mathematic-physical modelling, similarity and modelling and of classic and artificial intelligence methods necessary for model realization.
Student will be able to create mathematical models of selected real processes with aim of classic simulation programs and with artificial neural networks exploitation
|
| Course Contents |
|---|
1. Introduction to systems modelling, forms of description of dynamic system.
2. Basic types of modelling (physical, mathematical, cybernetic.)
3. Classification of models according to different viewpoints.
4. Mathematical modelling, analytical and experimental methods of
identification of mathematical system description.
5. Simulation of systems, creation of system model, block diagrams.
6. Simulation program SIMULINK, creation of simulation models.
7. Introduction to similarity and modelling theory, theorems of similarity.
8. Derivation of general criterion equation by analysis of ratio equations.
9. Derivation of general criterion equation using dimensional analysis.
10. Unconventional modelling - artificial intelligence (fuzzy models,
artificial neural networks, genetic algorithms).
11. Introduction to neural networks, neuron models, neural network.
12. Learning and generalization of neural networks, learning algorithms.
13. Creation of neural networks models in software tools NEUREX, Statistica
Neural Networks, MATLAB Neural Networks Toolbox.
|
| Recommended or Required Reading |
|---|
| Required Reading: |
|---|
JANČÍKOVÁ, Z. Modelling and simulation. Ostrava: VŠB-TU Ostrava, 2015.
RUSSELL, S. J. and P. NORVIG. Artificial intelligence: a modern approach. 3rd ed., Pearson new international ed. Harlow: Pearson, c2014. ISBN 978-1-292-02420-2.
|
KOŠTIALOVÁ JANČÍKOVÁ, Z. Modelování a simulace. Ostrava: VŠB - Technická univerzita Ostrava, 2017
FÁBRY, J. Matematické modelování. Praha: Professional Publishing, 2011. ISBN 978-80-7431-066-9.
DUŠEK, F. MATLAB a SIMULINK: úvod do používání. Pardubice: Univerzita Pardubice, 2000. ISBN 80-7194-273-1.
JANČÍKOVÁ, Z. Modelling and simulation. Ostrava: VŠB-TU Ostrava, 2015.
RUSSELL, S. J. and P. NORVIG. Artificial intelligence: a modern approach. 3rd ed., Pearson new international ed. Harlow: Pearson, c2014. ISBN 978-1-292-02420-2.
|
| Recommended Reading: |
|---|
CLOSE, Ch. M., D. K. FREDERICK a Jonathan C. NEWELL. Modeling and analysis of dynamic systems. 3rd ed. New York: Wiley, c2002. ISBN 0-471-39442-4.
|
JANČÍKOVÁ, Z. Umělé neuronové sítě v materiálovém inženýrství. Ostrava: VŠB - Technická univerzita Ostrava, Fakulta metalurgie a materiálového inženýrství, 2006. ISBN 80-248-1174-X.
NOSKIEVIČ, P. Modelování a identifikace systémů. Ostrava: Montanex, 1999. ISBN 80-7225-030-2.
CLOSE, Ch. M., D. K. FREDERICK a Jonathan C. NEWELL. Modeling and analysis of dynamic systems. 3rd ed. New York: Wiley, c2002. ISBN 0-471-39442-4.
|
| Planned learning activities and teaching methods |
|---|
| Lectures, Tutorials, Project work |
| Assesment methods and criteria |
|---|
| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
|---|
| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | | |
| Examination | Examination | 100 | 51 |