Course detail

Advanced Methods of Analyses and Simulation

ÚSI-RSPMOAcad. year: 2025/2026

The content of the subject “Advanced Methods of Analysis and Simulation” familiarises students with some non-standard advanced analysis methods and modelling techniques developed in order to support corporate decision-making. It focuses on issues present in the field of risk engineering via the explanation of theoretical principles, and involves students in learning to work with such theories, and their applications.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Entry knowledge

Knowledge from areas of mathematics (linear algebra, vectors, functional analysis, matrix operations) and statistics (analysis of time series, regression analysis, the use of statistical methods in economics).

Rules for evaluation and completion of the course

Exam: written test Exercises: Submission of seminar paper.

Attendance form: 80% face-to face, 20% distance learning.

Aims

The aim of the course is to make students familiar with analysis methods and simulation techniques (fuzzy logic, artificial neural networks, and genetic algorithms) via the explanation of the principles of these theories and their resulting applications in corporate decision-making, with a focus on the issues of risk management.
The knowledge and skills obtained from the course will enable students to apply a high-quality and modern approach to analysis and simulation (in the state and private sectors of the economy, organizations, firms, companies, banks, etc.) in order to support corporate decision-making, with a focus on the issues of risk management.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

DOSTÁL, P. Advanced Decision Making in Business and Public Services. Brno: CERM Akademické nakladatelství, 2013. 168 p. ISBN: 978-80-7204-747-5
DOSTÁL, P. Pokročilé metody rozhodování v podnikatelství a veřejné správě. Brno: CERM Akademické nakladatelství, 2012. 718 p. ISBN 978-80-7204-798-7, e-ISBN 978-80-7204-799-4.
DOSTÁL, P., SOJKA, Z. Financial Risk management, Zlín 2008, 80s., ISBN 978-80-7318-772-9.

Recommended reading

ALTROCK,C. Fuzzy Logic &Neurofuzzy, Book & Cd Edition, 1996, 375 s., ISBN 0-13-591512-0
BROZ, Z., DOSTÁL, P. Multilingual dictionary of artificial intelligence. Brno: CERM Akademické nakladatelství, 2012. 142 p. ISBN 978-80-7204-800-7, e-ISBN 978-80-7204-801-4.
DAVIS,L. Handbook of Genetic Algorithms, Int. Thomson Com. Press, 1991, 385 s., ISBN 1-850-32825-0
DOSTÁL, P. The Use of Optimization Methods in Business and Public Services. In Zelinka, I., Snášel, V., Abraham, A. Handbook of Optimization, USA: Springer, 2012. ISBN 978-3-642-30503-0.
GATELY, E. Neural Network for Financial Forecasting, John Wiley & Sons Inc., 1996, 169 s., ISBN 0-471-11212-7
Matlab The MathWorks Inc., US, 2017
PETERS, E. Fractal Market Analysis, John Wiley & Sons Inc., 1994, 315 s., SBN 0-471-58524-6
REBEIRO,R.R., ZIMMERMANN,H.J. Soft Computing in Fin.Engineering, Spring Verlag Comp.,1999,509s.,ISBN3-7908-1173-4.
SMEJKAL,V., RAIS, K. Řízení rizik ve firmách a jiných organizacích, Grada, Publishing.,a.s. Grada, Praha, 2006.

Elearning

Classification of course in study plans

  • Programme NMSP-RRTES Master's

    specialization RRTS , 1 year of study, summer semester, compulsory, fundamental theoretical courses of the profile core
    specialization RRES , 1 year of study, summer semester, compulsory, fundamental theoretical courses of the profile core

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Introduction
2. Fuzzy logic - terory
3. Fuzzy logic + application – Excel
4. Fuzzy logic – application Matlab
5. Artificial neural network - teory
6. Artificial neural network + applications Matlab
7. Genetic algorithms - theory
8. Genetic algorithms + aplikace Matlab
9. Theory of chaos
10. Datamining
11. Time series, prediction, capital markets
12. Production control, risk management
13. Decision making

Exercise

26 hours, optionally

Teacher / Lecturer

Syllabus

Introductory information, seminar assignment
Fuzzy Logic I – Excel
Fuzzy Logic II – Excel
Individual processing of part of the seminar paper I
Introduction to MATLAB
Fuzzy Logic IV – MATLAB
Fuzzy Logic V – MATLAB
Fuzzy Logic VI – MATLAB
Individual processing of part of the seminar paper II
Neural networks, genetic algorithms
Seminar paper presentation I
Seminar paper presentation II
Credit

Elearning