title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÑÐ»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð½Ð° Ð±Ð°Ð·Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of the module of prediction of technological parameters on the basis of neural networks
Creators: name: ÐÐ¾Ð³Ð°Ð½, Ð¡ÑÐ°Ð½Ð¸ÑÐ»Ð°Ð²
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐµÐºÐºÑÑÐµÐ½ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
RNN
GRU
LSTM
TCN
darts
neural network
reccurent neural network
convolutional neural network
time series forecasting

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description: In this work was analyzed the problem of predicting technological parameters and defined a list of requirements for the architecture of artificial neural networks that could be used in the development of the module of prediction of technological parameters. Subsequently, experiments were conducted with artificial neural networks containing RNN, LSTM and GRU blocks, as well as with an artificial neural network containing TCN blocks. For formation of training and validation samples historical data on the course of the technological process were used. Based on the conducted experiments, it was concluded that with an equal number of adjustable parameters, the artificial neural network artificial TCN blocks allows to obtain the most accurate forecasts of technological parameters. Subsequently, a new model based on TCN blocks, which proved to be the most effective, was created, supplemented with a correction module, which increased the accuracy of prediction of all technological parameters. As a result, the module of technological parameters forecasting was developed, which can be used as a part of SCADA-system in order to increase reliability and economic efficiency of technological process. The accuracy of the obtained module of prediction of technological parameters can be increased.