Кафедри

Зараз показуємо 1 - 4 з 4

On constructing a random values generator of the input material flow for transport conveyor models based on neural network
(Видавничий дім "Гельветика", 2024) Pihnastyi, O. M.; Sobol, Maksym
This study examines a method for constructing a generator of random values of the input material flow to form a training data set for highly efficient transport conveyor models based on a neural network. A comparative analysis of the experimental, approximated and generated realizations for the input material flow of the conveyor type transport system is presented.
Predictive analysis of the interval material flow rates in transport conveyors based on experimental data
(2024) Pihnastyi, O. M.; Sobol, Maksym; Burduk, Anna
This 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.
Development of a Method for Generating Material Input Flow for Transport Conveyor Using Experimental Data
(CEUR Workshop Proceedings, 2023) Pihnastyi, Oleh; Sobol, Maksym; Burduk, Anna
This work is devoted to the development of a method for generating values of the input material flow of a transport conveyor based on experimental data. The experimental data are represented by a single realization of the material flow for a sufficiently large observation time interval. The statistical characteristics of the implementation of the input material flow are studied. To determine the values of the correlation function, the numerical integration method was used. To analyze statistical characteristics, dimensionless parameters are introduced that can be used to construct similarity criteria for input material flows. When constructing the generator of the input material flow, the canonical expansion of the random process in orthogonal functions is used. This decomposition allows transformations to be carried out over a stochastic input flow of material. It is assumed that the implementation of the input material flow is formed for the steady state of material extraction. As a zero approximation when constructing generators of the input material flow values, it is stipulated that random measurements in the canonical expansion have a normal distribution law. Orthogonal functions are represented by a normalized Fourier series. It is shown that centered random variables of the canonical expansion have dispersion values that are defined as expansion coefficients of the correlation function in a Fourier series. Analysis of the generated material flow realization shows that its values have a distribution close to the normal distribution. An example of realization using a random value generator for the input material flow is presented. The accuracy of the realization is determined by the number of terms in the Fourier series expansion and the accuracy of the numerical integration method.
Analysis of a Dataset for Modeling a Transport Conveyor
(2022) Pihnastyi, O. M.; Burduk, Anna
The 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.

Кафедри

Переглянути

Фільтри

Налаштування

Сортувати за

Результатів на сторінку

Результати пошуку