Genetic Algorithm approach for the prediction of business risks´ dynamics of enterprise

Author

Sirbiladze, Gia ; Kapanadze, Mikheil

Author_Institution

Dept. of Comput. Sci., Iv.Javakhishvili Tbilisi State Univ., Tbilisi, Georgia

fYear

2012

fDate

17-19 Oct. 2012

Firstpage

1

Lastpage

5

Abstract

This work deals with the problem of identification and modeling of Discrete Fuzzy Dynamic System (DFDS) with possibility uncertainty, using the technologies of Genetic Algorithms (GA). Applying the results from [5-9, 11-13,15,16,18-20], the fuzzy recurrent process, the source of which is expert knowledge reflections on the states of the evolutionary complex system, is constructed. The dynamics of DFDS is described and the constructed model is converted to the finite model. The DFDS transition operator is restored by means of expert data with possibility uncertainty. Obtained results are illustrated by the example for prediction and stopping problems for evaluations of the increasing business risks of the enterprise.

Keywords

corporate modelling; genetic algorithms; possibility theory; risk analysis; DFDS transition operator; GA; business risks; discrete fuzzy dynamic system; enterprise; evolutionary complex system; expert data; expert knowledge reflections; fuzzy recurrent process; genetic algorithm approach; possibility uncertainty; Biological cells; Business; Fuzzy systems; Genetic algorithms; Mathematical model; Sociology; Uncertainty; DFDS; business risks; genetic algorithm; identification of DFDS;

fLanguage

English

Publisher

ieee

Conference_Titel

Application of Information and Communication Technologies (AICT), 2012 6th International Conference on

Conference_Location

Tbilisi

Print_ISBN

978-1-4673-1739-9

Type

conf

DOI

10.1109/ICAICT.2012.6398507

Filename

6398507