Vol.9, No.2, May 2020.                                                                                                                                                                                     ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Analysis of Neural Networks for Predicting Time Series When Assessing Industrial Safety Risks


Sergey Ya. Nagibin, Dmitry I. Loskutov


© 2020 Dmitry I. Loskutov, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 9, Issue 2, Pages 477-483, ISSN 2217-8309, DOI: 10.18421/TEM92-08, May 2020.


Received: 10 March 2020.

Revised:   05 April 2020.
Accepted: 12 April 2020.
Published: 27 May 2020.




The paper describes the choice of an artificial neural network (ANN), the most effective for use in problems of modeling the behavior of complex dynamic systems with the subsequent solution of the forecast problem. The choice is made to implement a risk-based approach in the domestic trusted innovation umbrella system «Zodiac» when monitoring the industrial safety of the enterprises of the Fuel and Energy Complex (FEC).


Keywords –Coefficient of determination, industrial safety, Kullback-Leibler divergence, monitoring, neural network, risk-based approach, «Zodiac» software.



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