Course detail

Advanced Methods of Analyses and Simulation

ÚSI-2SBPAAcad. year: 2018/2019

The content of the subject "Advanced Methods of Analyses and Simulation" is to get acquainted with some advanced and non-standard methods of analysis and simulation techniques as a support of decision making in business focussed on problems of risk management by the method of explanation of these theories, to become familiar with these theories and their use.

Learning outcomes of the course unit

The obtained knowledge and skills of the subject will enable the graduates fine and modern access in the processes of analyses and simulation (in the national economy and private sector, organizations, firms, companies, banks, etc.) in order to support decision making in business focussed on problems of risk management.


The basic knowledge of mathematics.


Not applicable.

Recommended optional programme components

Not applicable.

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, 2013

Planned learning activities and teaching methods

Teaching is carried out through lectures and seminars. Lectures consist of interpretations of basic principles, methodology of given discipline, problems and their exemplary solutions. Seminars particularly support practical mastery of subject matter presented in lectures or assigned for individual study with the active participation of students.

Assesment methods and criteria linked to learning outcomes

To obtain a classified credit it will be required:
1) Active participation in the exercises, i.e. processing of at least 4 of the 5 thematic tasks in the individual exercises (1. FL Excel, 2. FL MATLAB, 3. NN, 4. GA, 5. theory of chaos).
2) At least 5 points from the written semester project (max. 10 points). The scope of the project will be about 8 - 12 pages with an individual focus of the student on practical problems leading to the solution using fuzzy logic theory, artificial neural networks or genetic algorithms. Details of the project will be presented at the first exercise and the work must be submitted by the end of the 10th semester week.

The exam is classified according to ECTS. It is in written form of closed questions with a score of 0-20 points. A: 20-19; B: 18-17; C: 16-15; D: 14-13; E: 12-10; F: 9-0.

Language of instruction


Work placements

Not applicable.

Course curriculum

1. Fuzzy logic (FL): To be familiar with the basic notions and fuzzy logic rules, creation of models. The presentation of cases of application of fuzzy logic in decision making processes e.g. managerial and investment decision making, prediction, etc.
2. Artificial neural networks (ANN): To be familiar with the basic notions in the area of artificial neural networks, presentation of the notation perceptron, multilayer neural network and their parameters. The applications cover investment decision making, estimations of the price of products, real properties, evaluation of value of client, etc.
3. Genetic algorithms (GA): To be familiar with the principles of genetics, the analogy between nature and math description that enables the solution of decision making of problems. The use in the area of optimization of wide spectrum of problems is mentioned - the optimization of investment strategy, production control, cutting plans, curve fitting, the solution of traveling salesman, cluster analyses, etc.
4. Chaos theory (CH): The theory deals with the possibilities of better description of economic phenomena than the classical methods do. The notion chaos, order and fractal are clarified, the use of this theory to determinate the level of chaos of measured and watched system is mentioned
5. The use of mentioned theories in datamining, prediction, production control, risk management and decision making.


The aim of the course is to make students familiar with the methods of analyses and simulation techniques (fuzzy logic, artificial neural networks, and genetic algorithms) by the way of explanation of the principles of these theories and their resulting applications in decision making in business focussed to problems of risk management.

Specification of controlled education, way of implementation and compensation for absences

Attendance at lectures is not checked. Participation in the exercises is mandatory and is systematically checked. The student is obliged to excuse their absence. An absence must be compensated by processing the missed assignment and its presentation to the instructor in the next exercise. For the entire semester, the student has to write at least 4 of 5 assignments, either on a seminar, or individually with a subsequent personal presentation to the instructor.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MMI , any year of study, summer semester, 4 credits, compulsory-optional

  • Programme MRzI Master's

    branch REZ , 1. year of study, summer semester, 4 credits, compulsory
    branch RSZ , 1. year of study, summer semester, 4 credits, compulsory
    branch RFI , 1. year of study, summer semester, 4 credits, compulsory
    branch RSK , 1. year of study, summer semester, 4 credits, compulsory
    branch RIS , 1. year of study, summer semester, 4 credits, compulsory
    branch RCH , 1. year of study, summer semester, 4 credits, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


26 hours, compulsory

Teacher / Lecturer