NMS Numerical modelling and simulation

Institute of Technology and Business in České Budějovice
summer 2025
Extent and Intensity
1/2. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Robert Frischer, Ph.D. (seminar tutor)
prof. Ing. Zora Koštialová Jančíková, CSc. (seminar tutor)
Guaranteed by
prof. Ing. Zora Koštialová Jančíková, CSc.
Department of Applied Technologies and Materials Research – Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice
Supplier department: Department of Applied Technologies and Materials Research – Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice
Timetable of Seminar Groups
NMS/P01: Tue 13:50–14:35 I314, R. Frischer, Z. Koštialová Jančíková
NMS/S01: Tue 14:50–16:20 I314, R. Frischer, Z. Koštialová Jančíková
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives supported by learning outcomes
The aim of the course is to familiarize students with methods of realization of simulation models of dynamic systems. The interpretation is based on mathematical description of a dynamical system. Students are introduced to the principles of mathematical and physical modelling and the methods needed to implement the model on a digital computer. An introduction to artificial intelligence (fuzzy models, expert models, neural network models, genetic algorithms), attention is paid in particular to neural network models and their application to selected technological processes.
Learning outcomes
The student is able to define and describe basic classical methods of system identification and artificial intelligence methods for obtain a mathematical description of systems and is able to use these methods to design and implement simulation models on digital computer. The student is able to build mathematical models of selected real processes using classical simulation programs. and using artificial neural networks.
Syllabus
  • 1. Introduction to systems modelling. Forms of mathematical description of a system.
  • 2. Basic types of modelling (physical, mathematical, cybernetic). properties, tables of the Laplace transform.
  • 3. Classification of models according to different aspects. Solving linear differential equations using Laplace transformation, transfer of continuous functions.
  • 4. Mathematical modelling, analytical and experimental methods of identification. Classification of systems by order Linear differential equations.
  • 5. Simulation of systems, main stages of the modelling and simulation process, construction and verification of simulation models. Simulation programs - classification, description, examples of use.
  • 6. Static and dynamic characteristics of systems. System model creation, block diagrams.
  • 7. Introduction to artificial intelligence (fuzzy models, artificial neural networks, genetic algorithms). Method of order reduction system model.
  • 8. Theory of fuzzy sets, fuzzy modelling. Simulation program SIMULINK - characteristics, description.
  • 9. Artificial neural networks, neuron model. Simulation program SIMULINK - building simulation models, examples.
  • 10. Learning and generalization of neural networks, learning algorithms. Creating models of selected technological processes. in the simulation program SIMULINK.
  • 11. Neural network models, multilayer neural networks. Creation of fuzzy models in simulation programs.
  • 12. Factors affecting neural network learning. Creating neural network models in simulation programs.
  • 13. Use of neural networks for modelling of selected technological processes. Creation of neural models of selected technological processes.
Literature
    required literature
  • KOŠTIALOVÁ JANČÍKOVÁ, Z., 2022. Modelování a simulace. Ostrava: VŠB - Technická univerzita Ostrava.
  • KOŠTIALOVÁ JANČÍKOVÁ, Z., 2021. Modelling and simulation. Ostrava: VŠB - Technická univerzita Ostrava.
    recommended literature
  • JANČÍKOVÁ, Z., 2006. Umělé neuronové sítě v materiálovém inženýrství. Ostrava: VŠB - Technická univerzita Ostrava. ISBN 80-248-1174-X.
  • FÁBRY, J., 2011. Matematické modelování. Praha: Professional Publishing. ISBN 978-80-7431-066-9.
  • DUŠEK, F., 2000. MATLAB a SIMULINK: úvod do používání. Pardubice: Univerzita Pardubice. ISBN 80-7194-273-1.
  • CLOSE, Ch. M., FREDERICK, D. K. a J. C. NEWELL, 2002. Modeling and analysis of dynamic systems. 3rd ed. New York: Wiley. ISBN 0-471-39442-4.
Forms of Teaching
Lecture
Seminar
Student Workload
ActivitiesNumber of Hours of Study Workload
Daily StudyCombined Study
Preparation for the Mid-term Test10 
Příprava na závěrečnou zkoušku (in Czech)30 
Samostudium (in Czech)25 
Attendance on Lectures13 
Attendance on Seminars/Exercises/Tutorial/Excursion26 
Total:1040
Assessment Methods and Assesment Rate
Exam – oral 70 %
Test – mid-term 30 %
Exam conditions
To successfully complete the course, it is necessary to achieve the sum of the continuous and final assessment of at least 70% under the conditions set out below. V~30 points can be obtained in the continuous assessment, i.e. 30%. In the final assessment, it is possible to a total of 70 points, i.e. 70 %. Intermediate evaluation Intermediate test - 30 points (i.e. 30 %) Final assessment Final test - 70 points (i.e. 70%) A 100 – 90, B 89,99 – 84, C 83,99 – 77, D 76,99 – 73, E 72,99 – 70, FX 69,99 – 30, F 29,99 – 0
Language of instruction
Czech
Teacher's information
A full-time student is obliged to attend contact classes, i.e. everything except lectures, meet the mandatory 70% attendance.
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