Accident Damage Prevention Technology
Anna A. Burdina1, Anna A. Nekhrest-Bobkova2, Boris A.Gorelov3, Stanislav S.Burdin4
1Anna A. Burdina, Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, A-80, Postal service-3, 125993, Moscow, Russia.
2Anna A. Nekhrest-Bobkova, Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, A-80, Postal service-3, 125993, Moscow, Russia.
3Boris A.Gorelov, Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, A-80, Postal service-3, 125993, Moscow, Russia.
4Stanislav S.Burdin, Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, A-80, Postal service, Moscow, Russia.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4260-4263 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8312038620/2020©BEIESP | DOI: 10.35940/ijrte.F8312.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper studies the methods of neural network modeling to prevent damage from accidents, compares different approaches to the analysis of time series, explores the mechanisms for estimating the accuracy of forecasting values, describes the models and uses them. The problem of choosing the optimal prevent damage from accidents model according to minimum forecast criterion error is stated and solved. To solve this problem, there was used the group of mathematical methods, including statistics and econometrics, such as: autoregression, moving average, exponential smoothing, and neural network modeling. The result of the study is an algorithm for estimation of possible accident damage. The model is based on minimizing the forecasting error and implements created algorithm.
Keywords: Autoregression, ARIMA, Exponential Smoothing, Forecast Evaluation, Moving Average, Neural Networks, Accident Damage, Time Series.
Scope of the Article: Nondestructive Testing and Evaluation.