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Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/35393
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Документ Predictive analysis of the interval material flow rates in transport conveyors based on experimental data(2024) Pihnastyi, O. M.; Sobol, Maksym; Burduk, AnnaThis study examines a method for constructing a generator of random values of the input flow of material to form a training data set in highly efficient transport conveyor models based on a neural network. Dimensionless parameters are introduced that make it possible to represent the model of the input material flow of a transport conveyor in a dimensionless form. Coordinate functions are determined to approximate the experimental realization of the input material flow. A canonical decomposition of the experimental realization of the input material flow in terms of coordinate functions based on the use of fixed intervals is presented. For the selected canonical decomposition of the experimental realization of the input material flow, a theoretical correlation function is determined. It is shown that as the number of intervals increases, the correlation function of the experimental realization tends to the theoretical correlation function. The stages of constructing a random value generator for the input material flow are presented in detail. A comparative analysis of the experimental, approximated and generated realization for the input material flow is presented and estimates of the statistical characteristics of the realizations of the input material flow are given. The correlation functions constructed for the experimental, approximated and generated realizations of the input material flow are analyzed. An estimate is given of the length of the time interval required to carry out experimental changes in the input material flow.Документ Analysis of a Dataset for Modeling a Transport Conveyor(2022) Pihnastyi, O. M.; Burduk, AnnaThe analysis of the works, which considered the use of neural networks for modeling a multi-section transport conveyor, was carried out. The prospects for the use of neural networks for the design of highly efficient control systems for the flow parameters of a multi-section transport conveyor are studied. The problem that limits the use of neural networks for building control systems for the flow parameters of a multi-section transport conveyor is considered. The possibility of constructing generators for generating a data set for the process of training a neural network is being studied. A method for generating a data set based on experimentally obtained measurements of the instantaneous values of the input material flow as a result of the operation of industrial transport systems is proposed. Using dimensionless variables, a statistical analysis of a stochastic flow of material entering the input of the transport system was performed. An estimate of the correlation time of a stochastic process characterizing the input flow of material is given. The recommendations on choosing the type of correlation function for the model of the input material flow were confirmed. It is demonstrated that the input flow of material is a non-stationary stochastic process. Approximations for modeling the input flow of materials of the operating transport system are considered.