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Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/35393
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Документ Predicting the parameters of the output flow of conveyor systems(Institute of Electrical and Electronics Engineers, Inc., USA, 2021) Pihnastyi, O. M.; Kozhevnikov, G. K.; Ivanovska, O. V.In this article, the problem of constructing models of multi-section conveyor-type transport systems using a neural network is considered. The analysis of models of long-distance multi-section transport systems, which are used to design control systems of the flow parameters from the point of view of reducing the unit costs of material transportation, is presented. The areas of the models' application and associated limitations are demonstrated. The advantages of using neural networks for developing multi-section transport systems models are shown. The influence of the initial distribution of materials along the transport route and the presence of a transport delay in the system on the quality of prediction of the transport system flow parameters are estimated. The effect of the use of speed sensors located on the inner and output sections in order to improve the quality of prediction of the transport system flow parameters is analyzed. It is shown that the use of speed sensors in conveyors with belt speed control can significantly improve the quality of predict.Документ Improving the Prediction Quality for a Multi-Section Transport Conveyor Model Based on a Neural Network(2022) Pihnastyi, O. M.; Ivanovska, O. V.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 сriterion 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.