Paste-filling weighing control system optimization based on neural network
Abstract
Purpose
This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system.
Design/methodology/approach
The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error.
Findings
Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes.
Originality/value
The method can be further used to improve the control precision of the coal mine paste-filling system.
Keywords
Acknowledgements
This project was supported by the Science and Technology project of Hebei Province (16394102D).
Citation
Wang, G., Zhang, Y.S., Yang, L. and Zhang, S. (2017), "Paste-filling weighing control system optimization based on neural network", World Journal of Engineering, Vol. 14 No. 2, pp. 155-158. https://doi.org/10.1108/WJE-10-2016-0108
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited