Course detail

Advanced Methods of Analyses and Simulation

FP-FpmamPAcad. year: 2014/2015

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

4

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 knowledge in the area of math (linear algebra, arrays, analyses of functions, operations with matrixes) statistics (analysis of time series, regression analyses, the use of statistical methods in economy), operational analysis (linear programming), financial analyses and planning (the analyses of profits and costs, cash flow, value and bankruptcy model).

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

The credit will be granted in case of an active participation in trainings and handing in the assignment. The work will range approximately from 8 to 12 pages concentrating on individual problem from practice leading to solution with the help of theory of fuzzy logic, artificial neural network or genetic algorithms. The exam will be in the form of a test. The classification will be made on the basis of the correctness of answers in the test together with consideration of the quality of the assignment.

Course curriculum

1,2. 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.
3,4. 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.
5,6. 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.
7. Chaos theory: 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
8. Data mining: The notion data mining, the definition of aims, the selection of methods of simulation, sources and preparation of data, creation of models, their verification, evaluation, implementation and maintenance are mentioned there. The presentation of the cases of the use for strategy of cooperation with customer, direct mailing, etc.
9. Simulation: The presentation of the notion simulation and its identification and simulation. The use of FL, UNS a GA as a mean of simulation.
10. Prediction: The presentation of methods of prediction of time series by means of FL, ANN and GA and their use for prediction of future development of various economic values in practice.
11. Stock market: The presentation of the notion stock market. The description of the use of FL, UNS a GA on stock market.
12. Decision making: The presentation of the notion decision making. The description of the use of FL, UNS a GA for decision making processes.
13. Summary

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

The participation in lectures is not checked. The participation in trainings is compulsory and is systematically checked. The students are supposed to excuse for their absence. The teacher judges the reason of excuse. The way of substitution of a missed training will be set by the teacher individually.

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, Akademické nakladatelství CERM, 2008 Brno,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., 2011. (EN)

Recommended reading

DOSTÁL, P. The Use of Soft Computing for Optimization in Business, Economics, and Finance. In Vasant, P. Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, USA: IGI Globe, 2012. DOI: 10.4018/978-1-4666-2086-5, ISBN13: 9781466620865, ISBN10: 1466620862, EISBN13: 9781466620872. (EN)
DOSTÁL, P. The Use of Soft Computing in Management. In Vasant, P. Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications USA: IGI Globe, 2013. DOI: 10.4018/978-1-4666-4450-2, ISBN13: 9781466644502, ISBN10: 1466644508, EISBN13: 9781466644519. (EN)

Classification of course in study plans

  • Programme MGR Master's

    branch MGR-UFRP , 1 year of study, summer semester, elective

Type of course unit

 

Lecture

20 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

Exercise

10 hod., compulsory

Teacher / Lecturer

Syllabus

1. Fuzzy logic - Excel
2. Fuzzy logic - Assignment I.
3. Fuzzy logic - Assignment II.
4. Fuzzy logic - MATLAB
5. Credit