| id: | 27733 |
|---|---|
| Title: | Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks |
| Authors: | Havlovska N., Koptieva H., Babchynska O., Rudnichenko Y., Lopatovskyi V., Prytys V. |
| Keywords: | managerial decision, economic security, risk, benefit, neural network |
| Date of publication: | 2022-08-03 12:38:37 |
| Last changes: | 2022-08-03 12:38:37 |
| Year of publication: | 2022 |
| Summary: | 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 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. |
| URI: | http://ir.vtei.edu.ua/card.php?id=27733 |
| Publication type: | Стаття Scopus |
| Publication: | TEM Journal. 2022. Vol. 11, Iss. 1. P. 13-23 |
| In the collections: | Публікація у Scopus/ Публікації в наукометричних базах даних/ Видання інших установ/ |
| Published by: | Адміністратор |
| File : 27733.pdf Size : 577861 byte Format : Adobe PDF Access : For all | |
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