Analysis of a Dataset for Modeling a Transport Conveyor

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

Control of a Conveyor Based on a Neural Network

The present study is devoted to the design of the main flow parameters of a conveyor control system with a large number of sections. For the design of the control system, a neural network is used. The architecture of the neural network is justified and the rules for the formation of nodes for the input and output layers are defined. The main parameters of the model are identified and analyzed. The data set for training the neural network is formed using the analytical model of the transport system. The criterion for the quality of the transport system is written. For the given criterion for the quality of the transport system, the Pontryagin function is defined and the adjoint system of equations is given. It allows calculating optimal control of the transport system. For calculation is used additional model of the transport system with output nodes which are controls. A graphical representation of the results of the study is give

Neural model of conveyor type transport system

In this paper, a model of a transport conveyor system using a neural network is demonstrated. The analysis of the main parameters of modern conveyor systems is presented. The main models of the conveyor section, which are used for the design of control systems for flow parameters, are considered. The necessity of using neural networks in the design of conveyor transport control systems is substantiated. A review of conveyor models using a neural network is performed. The conditions of applicability of models using neural networks to describe conveyor systems are determined. A comparative analysis of the analytical model of the conveyor section and the model using the neural network is performed. The technique of forming a set of test data for the process of training a neural network is presented. The foundation for the formation of test data for learning neural network is an analytical model of the conveyor section. Using an analytical model allowed us to form a set of test data for transient dynamic modes of functioning of the transport system. The transport system is presented in the form of a directed graph without cycles. Analysis of the model using a neural network showed a high-quality relationship between the output flow for different conveyor sections of the transport syste