University proceedings. Volga region. Technical sciences
Background. Regression analysis is a type of machine learning. With the application of regression analysis, the problems of structural-parametric identification, and predicting the behavior of systems and objects are solved. Regression models constructed using observed data at finite time intervals...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penza State University Publishing House
2024-12-01
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| Series: | Известия высших учебных заведений. Поволжский регион:Технические науки |
| Subjects: | |
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| Summary: | Background. Regression analysis is a type of machine learning. With the application of regression analysis, the problems of structural-parametric identification, and predicting the behavior of systems and objects are solved. Regression models constructed using observed data at finite time intervals are time series models. The purpose of the study, the results of which are presented in the article, is to develop non-parametric regression models for the analysis and forecasting of fires, tragic events on the water, accidents on water pipes, road traffic accidents in the region. Materials and methods. Analysis and forecasting of time series levels reflecting emergencies and events in the region, by machine learning using nonparametric regression models based on linear and nonlinear functions of activation of artificial neurons. Results.The analysis and prediction of the time series levels are set and solved. Content of the problem: analysis of the stagnation of the
time series; elimination of random emissions; allocation of the piece-line trend; development of non-parametric models of machine learning; performance of a omputational experiment and evaluation of the quality of forecasting. Conclusions. The results of the computational experiments confirmed the prospects of applying the machine learning method using nonparametric regression models based on Fourier functions, piecelinear and nonlinear functions. |
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| ISSN: | 2072-3059 |