Artificial intelligence components and fuzzy regulators in entrepreneurship development

Sergii Bogachov (), Aleksy Kwilinski (), Boris Miethlich, Viera Bartosova and Aleksandr Gurnak
Additional contact information
Sergii Bogachov: PO "Institute for Local and Regional Development", Ukraine
Aleksy Kwilinski: London Academy of Science and Business, United Kingdom
Boris Miethlich: Comenius University in Bratislava, Slovakia
Viera Bartosova: University of Žilina, Slovakia
Aleksandr Gurnak: Financial University under the Government of the Russian Federation, Russian Federation

Entrepreneurship and Sustainability Issues, 2020, vol. 8, issue 2, 487-499

Abstract: The article provides a comparative study of the possibility of entrepreneurship development based on fuzzy signals of business activity and applied elements of artificial intelligence. The principal research methods that determine the logic and practical basis of the application of fuzzy logic in entrepreneurship are highlighted. It has been determined that fuzzy modeling is effective when technological processes are too complex for analysis using generally accepted quantitative methods, or when available sources of information in the business environment are interpreted poorly, inaccurately, and indefinitely. It has been shown experimentally that fuzzy logic gives better results compared to those obtained with generally accepted algorithms for analyzing the quality of doing business. A model of a neuro-fuzzy regulator has been developed and measures for its implementation in the business environment have been proposed. A neural network model in entrepreneurial development has been formed. Studies have shown the possibility of effective use of the principles of artificial intelligence and modeling in solving problems of developing entrepreneurial potential and making business decisions under conditions of uncertainty. This ensures objective and well-grounded decision-making in solving various applied problems of business development and taking into account environmental factors. The applied tasks of supporting the adoption of entrepreneurial decisions in the conditions are formulated; uncertainty; indicating that approaches to decision-making under conditions of uncertainty based on artificial intelligence and fuzzy logic tools are universal and require appropriate careful study and adaptation to a specific applied problem in the business environment.

Keywords: entrepreneurship; neural network; regulators; linguistic rule; genetic algorithm; object of control (search for similar items in EconPapers)
JEL-codes: M21 O16 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://jssidoi.org/jesi/uploads/articles/30/Bogac ... ship_development.pdf (application/pdf)
https://jssidoi.org/jesi/article/711 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:8:y:2020:i:2:p:487-499

DOI: 10.9770/jesi.2020.8.2(29)

Access Statistics for this article

Entrepreneurship and Sustainability Issues is currently edited by Manuela Tvaronaviciene

More articles in Entrepreneurship and Sustainability Issues from VsI Entrepreneurship and Sustainability Center
Bibliographic data for series maintained by Manuela Tvaronaviciene ().