Improving the Prediction Quality for a Multi-Section Transport Conveyor Model Based on a Neural Network
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2022
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DOI
ะะฐัะบะพะฒะธะน ัััะฟัะฝั
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The multi-section transport conveyor model based on the neural network for predicting the output flow parameters is considered. The expediency of using sequential and batch modes of training of a neural network in a model of a multi-section transport conveyor has been investigated. The quality ัriterion of predicting the output flow parameters of the transport system is written. Comparative analysis of sequential and batch modes of neural network training is carried out. The convergence of the neural network training process for different sizes of the training batch is studied. The effect of the batch size on the convergence rate of the neural network learning process is estimated. The results of predicting the output flow parameters of a multi-section transport system for models based on a neural network that was learned using training batches of different sizes are presented. A nonlinear relationship between the batch size and the convergence rate of the neural network learning process is demonstrated. The recommendations are given on the choice of learning modes for a neural network in the model of a multi-section transport conveyor. The choice of the initialization value of the node participating in the formation of the bias value is investigated. The qualitative regularities characterizing the influence of the choice of the node initialization value on the forecasting accuracy of the output flow parameters of the transport system are studied.
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multi-section conveyor, distributed transport system, conveyor belt, belt speed control, accumulation bunker, bias, sequential mode, batch mode
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Pihnastyi O. Improving the Prediction Quality for a Multi-Section Transport Conveyor Model Based on a Neural Network [Electronic resource] / O. Pihnastyi, O. Ivanovska // CEUR Workshop Proceedings. โ 2021. โ Vol. 3132. โ Information Technology and Implementation (IT&I-2021) : sel. papers of the 8th Intern. Sci. Conf., Kyiv, Ukraine, December 01-03, 2021 / ed.: A. Anisimov [et al.] ; Taras Shevchenko Nation. Univ. of Kyiv [et al.]. โ Electronic text data. โ Kyiv, 2021. โ P. 24-38. โ URL: http://ceur-ws.org/Vol-3132/Paper_3.pdf, free (accessed 28.05.2022).