Title:  Advanced Methods of Analyses and Simulation 

Code:  PMM (FP RpmamP) 

Ac.Year:  2017/2018 

Term:  Summer 

Curriculums:  

Language:  Czech 

Credits:  4 

Completion:  classified credit 

Type of instruction:  Hour/sem  Lectures  Sem. Exercises  Lab. exercises  Comp. exercises  Other 

Hours:  26  13  0  0  0 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  0  0  100  0  0 



Guarantee:  Dostál Petr, prof. Ing., CSc., II 

Lecturer:  Dostál Petr, prof. Ing., CSc., II 
Faculty:  Faculty of Business and Management BUT 

 Learning objectives: 

  The aim of the course is to get acquainted with some advanced and nonstandard 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.  Description: 

  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.  Knowledge and skills required for the course: 

  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).  Learning outcomes and competences: 

  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.  Syllabus of lectures: 

 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: 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. 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
6. Simulation: The presentation of the notion system and its
identification and simulation. The description of the use of FL, ANN and
GA during the process of simulation of decision making processes in
enterprise sphere.  Progress assessment: 

  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.  Exam prerequisites: 

  The credit will be granted in case of an active participation in
trainings and handing in the final assignment, in case of need the
written test. 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 classified credit will be classified according ECTS. The way of
implementation is in the form of test with in the range 020 points.
A2019;B1817;C1615;D1413;E12;F100.  
