Pihnastyi, Oleh and Sytnikova, Anastasiya (2021): Construction of Control Systems of Flow Parameters of the Smart Conveyor using a Neural Network. Published in: Central European Researchers Journal , Vol. 7, No. 2 (3 September 2021): pp. 1-14.
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Abstract
In this paper, the results of the model for forecasting the flow parameters of a distributed transport system of the conveyor type are briefly considered. It is shown that the model of the transport system based on the neural network can be successfully applied to predict the flow parameters of the transport system which consists of a very large number of sections.
Item Type: | MPRA Paper |
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Original Title: | Construction of Control Systems of Flow Parameters of the Smart Conveyor using a Neural Network |
Language: | English |
Keywords: | conveyor; forecasting model; neural network |
Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity L - Industrial Organization > L2 - Firm Objectives, Organization, and Behavior > L23 - Organization of Production Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation > Q21 - Demand and Supply ; Prices |
Item ID: | 109770 |
Depositing User: | Oleh Mikhalovych Pihnastyi |
Date Deposited: | 18 Sep 2021 14:17 |
Last Modified: | 18 Sep 2021 14:17 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/109770 |