Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks
Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks
Author(s): Nataliia Havlovska, Hanna Koptieva, Olena Babchynska, Yevhenii Rudnichenko, Viktor Lopatovskyi, Vadym PrytysSubject(s): Business Economy / Management, ICT Information and Communications Technologies
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: managerial decision; economic security; risk; benefit; neural network
Summary/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.
Journal: TEM Journal
- Issue Year: 11/2022
- Issue No: 1
- Page Range: 13-23
- Page Count: 11
- Language: English