Rocznik Ochrona Środowiska 2022, vol. 24, pp. 26-40
Vasyl Kalinchyk 
, Olexandr Meita , Vitalii Pobigaylo , Olena Borychenko , Vitalii Kalinchyk
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National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine |
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https://doi.org/10.54740/ros.2022.003 |
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This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes, and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognize the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for the tasks of identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for the construction of neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters is made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification on the basis of artificial neural networks is offered.
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classification, modelling, neural network, networks with radial basis functions, RBF, multilayer perceptron, MLP
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AMA Style
Kalinchyk V, Meita O, Pobigaylo V, Borychenko O, Kalinchyk V. Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex. Rocznik Ochrona Środowiska. 2022; 24. https://doi.org/10.54740/ros.2022.003
ACM Style
Kalinchyk, V., Meita, O., Pobigaylo, V., Borychenko, O., Kalinchyk, V. 2022. Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex. Rocznik Ochrona Środowiska. 24. DOI:https://doi.org/10.54740/ros.2022.003
ACS Style
Kalinchyk, V.; Meita, O.; Pobigaylo, V.; Borychenko, O.; Kalinchyk, V. Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex Rocznik Ochrona Środowiska 2022, 24, 26-40. https://doi.org/10.54740/ros.2022.003
APA Style
Kalinchyk, V., Meita, O., Pobigaylo, V., Borychenko, O., Kalinchyk, V. (2022). Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex. Rocznik Ochrona Środowiska, 24, 26-40. https://doi.org/10.54740/ros.2022.003
ABNT Style
KALINCHYK, V.; MEITA, O.; POBIGAYLO, V.; BORYCHENKO, O.; KALINCHYK, V. Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex. Rocznik Ochrona Środowiska, v. 24, p. 26-40, 2022. https://doi.org/10.54740/ros.2022.003
Chicago Style
Kalinchyk, Vasyl, Meita, Olexandr, Pobigaylo, Vitalii, Borychenko, Olena, Kalinchyk, Vitalii. 2022. "Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex". Rocznik Ochrona Środowiska 24, 26-40. https://doi.org/10.54740/ros.2022.003
Harvard Style
Kalinchyk, V., Meita, O., Pobigaylo, V., Borychenko, O., Kalinchyk, V. (2022) "Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex", Rocznik Ochrona Środowiska, 24, pp. 26-40. doi:https://doi.org/10.54740/ros.2022.003
IEEE Style
V. Kalinchyk, O. Meita, V. Pobigaylo, O. Borychenko, V. Kalinchyk, "Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex", RoczOchrSrod, vol 24, pp. 26-40. https://doi.org/10.54740/ros.2022.003
