Course detail

Advanced Methods of Decission

FP-IpmrPAcad. year: 2017/2018

The content of the subject 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 managerial practice.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Learning outcomes of the course unit

The obtained knowledge and skills of the subject will enable the graduates the top and modern access in the processes of analyses and simulation in the national economy and private sector, organizations, firms, companies, banks, etc., especially in managerial, but also in economical and financial sphere.

Prerequisites

The basic knowledge of mathematics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.

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.

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.

Work placements

Not applicable.

Aims

The aim of the course is to get acquainted with some advanced and non-standard methods of analysis and simulation techniques in economy and finance by the method of explanation of these theories, to become familiar with these theories and their use.

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

DOSTÁL, P.: Advanced Decision making in Business and Public Services, Akademické nakladatelství CERM, 2011 Brno,ISBN 978-80-7204-747-5. (EN)
DOSTÁL, P. Pokročilé metody analýz a modelování v podnikatelství a veřejné správě, CERM, 2008, 430s, ISBN 978-80-7204-605-8. (CS)
DOSTÁL, P, RAIS, K., SOJKA, Z.: Pokročilé metody manažerského rozhodování, Praha Grada, 2005., ISBN 80-247-1338-1. (CS)
THE MATHWORKS. MATLAB – User’s Guide, The MathWorks, Inc., 2021. (EN)

Recommended reading

ALTROCK,C. Fuzzy Logic &Neurofuzzy, Book & Cd Edition, 1996, 375 s., ISBN 0-13-591512-0 (EN)
DAVIS,L. Handbook of Genetic Algorithms, Int. Thomson Com. Press, 1991, 385 s., ISBN 1-850-32825-0 (EN)
GATELY, E. Neural Network for Financial Forecasting, John Wiley & Sons Inc., 1996, 169 s., ISBN 0-471-11212-7 (EN)
JANÍČEK, P. Systémové pojetí vybraných oborů pro techniky, CERM, Brno, 2007, 1234 s., ISBN 978-80-7204-554-9. (CS)
PETERS, E. Fractal Market Analysis, John Wiley & Sons Inc., 1994, 315 s., SBN 0-471-58524-6 (EN)
REBEIRO,R.R., ZIMMERMANN,H.J. Soft Computing in Fin. Engineering, Spring Verlag Comp.,1999,509s.,ISBN3-7908-1173-4. (EN)

Classification of course in study plans

  • Programme MGR-SI Master's

    branch MGR-IM , 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., 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

13 hod., compulsory

Teacher / Lecturer

Syllabus

1. Fuzzy logic – Excel
2. Fuzzy logikc – Excel + Assignment
3. Fuzzy logic – MATLAB
4. Neural networks
5. Genetic algorithms
6. Defence of Assignment
7. Credit