Open AccessModifying the method for forecasting hazardous processes with unknown dynamics in the presence of noise
Author(s) -
Борис Поспелов,
Vladimir Andronov,
Olekcii Krainiukov,
Kostiantyn Karpets,
Yuliia Bezuhla,
К.А. Фісун,
Svyatoslav Manzhura,
Svitlana Hryshko,
Olga A. Mukhina,
V. Ya. Ivanova
Publication year - 2022
Publication title -
eastern-european journal of enterprise technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.268
H-Index - 24
eISSN - 1729-4061
pISSN - 1729-3774
DOI - 10.15587/1729-4061.2022.252076
Subject(s) - noise (video) , masking (illustration) , gaussian noise , mean squared error , mathematics , standard deviation , amplitude , statistics , algorithm , computer science , artificial intelligence , art , visual arts , physics , quantum mechanics , image (mathematics)
This paper has substantiated a modified method that, within the framework of the adaptive zero-order Brown’s model, provides for increased accuracy in predicting processes with unknown dynamics masked by the noise of various levels. The forecasting method modification essentially involves an adaptive technique for determining the weight of the correction of the previous forecast, taking into consideration the recurrent state of the predicted process in time. To investigate the accuracy of the forecasting method, a test model of the process dynamics was determined in the form of a rectangular pulse with unit amplitude. In addition, a model of additive masking noise was defined in the form of a discrete Gaussian process with a zero mean and a variable value of the mean square deviation. Based on determining the exponentially smoothed values of current absolute forecasting errors, the dynamics of forecast accuracy were examined for the modified and self-adjusting methods. It was found that for the mean quadratic deviation of the masking noise equal to 0.9, the smoothed absolute prediction error for the modified method does not exceed 23 %; for the self-adjusting method – 42 %. This means that the prediction accuracy for the modified method is about twice as high. In the case of an average square deviation of masking noise of 0.1, the smoothed absolute prediction error for the modified and self-adjusting methods is approximately the same and does not exceed 10 %. That means that at a low level of masking noise, both prediction methods provide approximately the same accuracy. However, with an increase in the level of masking noise, the self-adjusting method significantly loses the accuracy of the forecast to the proposed modified method.