Neural model of conveyor type transport system
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Дата
2020
Автори
DOI
Науковий ступінь
Рівень дисертації
Шифр та назва спеціальності
Рада захисту
Установа захисту
Науковий керівник
Члени комітету
Видавець
Анотація
In this paper, a model of a transport conveyor system using a neural network is demonstrated. The analysis of the main parameters of modern conveyor systems is presented. The main models of the conveyor section, which are used for the design of control systems for flow parameters, are considered. The necessity of using neural networks in the design of conveyor transport control systems is substantiated. A review of conveyor models using a neural network is performed. The conditions of applicability of models using neural networks to describe conveyor systems are determined. A comparative analysis of the analytical model of the conveyor section and the model using the neural network is performed. The technique of forming a set of test data for the process of training a neural network is presented. The foundation for the formation of test data for learning neural network is an analytical model of the conveyor section. Using an analytical model allowed us to form a set of test data for transient dynamic modes of functioning of the transport system. The transport system is presented in the form of a directed graph without cycles. Analysis of the model using a neural network showed a high-quality relationship between the output flow for different conveyor sections of the transport system.
Опис
Ключові слова
production line, PDE-model of production, conveyor, distributed system, transport delay
Бібліографічний опис
Pihnastyi O. Neural model of conveyor type transport system [Electronic resource] / O. Pihnastyi, V. Khodusov // CEUR Workshop Proceedings. – 2020. – Vol. 2608. – Computer Modeling and Intelligent Systems (CMIS-2020) : proc. of the 3rd Intern. Workshop, Zaporizhzhia, Ukraine, April 27-May 1, 2020. – Electronic text data. – Germany, 2020. – P. 804-818. – URL: http://ceur-ws.org/Vol-2608/paper60.pdf, free (accessed 08.07.2020).