Czech

Not applicable.

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.

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.

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.

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.

Not applicable.

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.

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.

Not applicable.

Not applicable.

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.

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.
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GATELY, E. Neural Network for Financial Forecasting, John Wiley & Sons Inc., 1996, 169 s., ISBN 0-471-11212-7
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SMEJKAL,V., RAIS, K. Řízení rizik ve firmách a jiných organizacích, Grada, Publishing.,a.s. Grada, Praha, 2006.