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Title: Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks
Authors: Havlovska, Nataliia
Koptieva (Fadieieva), H. M.
Babchynska, Olena
Rudnichenko, Yevhenii
Lopatovskyi, Viktor
Prytys, Vadym
Keywords: managerial decision; economic security; risk; benefit; neural network
Issue Date: 2022
Publisher: UIKTEN - Association for Information Communication Technology Education and Science, Serbia
Citation: Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks / N. Havlovska [et al.] // TEM Journal. Technology, Education, Management, Informatics. – 2022. – Vol. 11, Iss. 1. – P. 13-23.
Abstract: Enterprise management is based on the need to make and justify management decisions that contribute to its development. It is almost impossible to determine the risk of a particular managerial decision, and excessive risk in the implementation of individual projects can lead to loss of business. Therefore, management faces the need to find a balance between benefits and risks, at which, on the one hand, it will be possible to develop a company and, on the other hand, adhere to postulates of safetyoriented management. Since management decisions cannot be foreseen for all possible situations and combinations of risk-benefit ratios, a universal model is proposed. It implies a golden ratio, depending on the limited number of current conditions, that would satisfy an enterprise management from the standpoint of sufficient justification on a decision. The article proposes a probabilistic neural network architecture and Matlab parameters of a probabilistic neural network for diagnosing the states of a safety-oriented control system. The proposed model in the form of a probabilistic neural network generates a response to input data on previous month under estimation, and forms an optimal state for a next month.
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