638-3002/03 – Modelling and Simulation (MS)

Gurantor departmentDepartment of Automation and Computing in IndustryCredits5
Subject guarantorprof. Ing. Zora Koštialová Jančíková, CSc.Subject version guarantorprof. Ing. Zora Koštialová Jančíková, CSc.
Study levelundergraduate or graduateRequirementCompulsory
Year1Semesterwinter
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFMTIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
JAN45 prof. Ing. Zora Koštialová Jančíková, CSc.
ZIM018 Ing. Ondřej Zimný, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 14+0

Subject aims expressed by acquired skills and competences

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

Teaching methods

Lectures
Tutorials
Project work

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.

Compulsory literature:

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 literature:

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.

Way of continuous check of knowledge in the course of semester

písemný test a ústní zkoušení

E-learning

Jančíková, Z. Modelling and simulation. Ostrava: VŠB-TU Ostrava, 2015 (https://www.fmmi.vsb.cz/cs/studenti/study-support/index.html)

Other requirements

active participation in seminars

Prerequisities

Subject codeAbbreviationTitleRequirement
638-2008 TS System Theory Recommended
638-2012 IS System Identification Recommended

Co-requisities

Subject has no co-requisities.

Subject syllabus:

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.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit  
        Examination Examination 100  51
Mandatory attendence parzicipation: Testing

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (N0413A270002) Quolity Management and Control of Industrial Systems (S03) Intelligent Control Systems in Industry P Czech Ostrava 1 Compulsory study plan
2021/2022 (N0413A270002) Quolity Management and Control of Industrial Systems (S03) Intelligent Control Systems in Industry K Czech Ostrava 1 Compulsory study plan
2020/2021 (N0413A270002) Quolity Management and Control of Industrial Systems (S03) Intelligent Control Systems in Industry K Czech Ostrava 1 Compulsory study plan
2020/2021 (N0413A270002) Quolity Management and Control of Industrial Systems (S03) Intelligent Control Systems in Industry P Czech Ostrava 1 Compulsory study plan
2019/2020 (N0413A270002) Quolity Management and Control of Industrial Systems (S03) Intelligent Control Systems in Industry P Czech Ostrava 1 Compulsory study plan
2019/2020 (N0413A270002) Quolity Management and Control of Industrial Systems (S03) Intelligent Control Systems in Industry K Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner