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.
Highlights
The problem of considering intellectual activity in entrepreneurship becomes a primary one, unlike the approach of designing traditional artificial intelligence systems from the 'bottom-up' principle based on "rigid models" of information management systems
The basic principle of building a fuzzy regulator in the entrepreneurship education is based on the experience formalization of the intelligent operator which manages a dynamic object by means of the linguistic rules "if ... " (Galbraith (2014)
It is determined that the fuzzy model of the regulator integrates the capabilities of the fuzzy system to incorporate knowledge in the form of linguistic rules set by the entrepreneur in the process of beginning development
Summary
The problem of considering intellectual activity in entrepreneurship becomes a primary one, unlike the approach of designing traditional artificial intelligence systems from the 'bottom-up' principle based on "rigid models" of information management systems. The basis of a new subject-oriented approach to the entrepreneurship development is research aimed at developing methods based on the selection of psychological features of managerial activity that can be introduced into the model of artificial intelligence (Axelrod, (1997). This position is based on the position of cognitive psychology and attempt to formalize actions typical for the entrepreneur in making commercial decisions and system development.
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