Advanced Methods of Analyses and Simulation
ÚSI-RSPMOAcad. year: 2020/2021
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.
Learning outcomes of the course unit
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.
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).
Recommended optional programme components
Recommended or required reading
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. Advanced Decision Making in Business and Public Services. Brno: CERM Akademické nakladatelství, 2013. 168 p. ISBN: 978-80-7204-747-5
DOSTÁL, P., SOJKA, Z. Financial Risk management, Zlín 2008, 80s., ISBN 978-80-7318-772-9.
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.
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.
SMEJKAL,V., RAIS, K. Řízení rizik ve firmách a jiných organizacích, Grada, Publishing.,a.s. Grada, Praha, 2006.
ALTROCK,C. Fuzzy Logic &Neurofuzzy, Book & Cd Edition, 1996, 375 s., ISBN 0-13-591512-0
GATELY, E. Neural Network for Financial Forecasting, John Wiley & Sons Inc., 1996, 169 s., ISBN 0-471-11212-7
DAVIS,L. Handbook of Genetic Algorithms, Int. Thomson Com. Press, 1991, 385 s., ISBN 1-850-32825-0
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.
Matlab The MathWorks Inc., US, 2017
Planned learning activities and teaching methods
Tuition takes place via lectures and seminars. The lectures focus on the explanation of basic principles, the methods of the given discipline, problems and example solutions. The seminars mainly support practical mastery of the subject matter presented in lectures or assigned for individual study with the active participation of students.
Assesment methods and criteria linked to learning outcomes
Language of instruction
1. Introduction and definition of the subject "Advanced methods of analysis and simulation”
2. Identification of basic terms in the area of analysis
3. Identification of the basic terms and rules of fuzzy logic, the creation of models
4. Presentation of case studies of the use of fuzzy logic in risk management
5. Identification of basic terms in the area of artificial neural networks
6. Presentation of case studies of the use of artificial neural networks in risk management
7. Identification of basic terms in the area of genetic algorithms
8. Presentation of case studies of the use of genetic algorithms in risk management
9. Methods of simulating prediction by means of fuzzy logic, neural networks and genetic algorithms
10. Introduction to chaos theory and its possible usage in risk management
11. The usage of software for solving problems
12. Introduction to issues related to simulation
13. Presentation of applications of the usage of simulation in risk management
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.
Specification of controlled education, way of implementation and compensation for absences
Attendance: 80% face-to face tuition, 20% distance learning.
Attendance in seminars will be checked - students must achieve at least 75% attendance in seminars.
Absence from seminars can be compensated for via a special assignment or test.