Application of BP Neural Network in the Prediction of Periodic Weighting

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Authors

  • Hongjun Guo
     Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China ,CN
  • Ming Ji
     Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China; School of Mines; China University of Mining & Technology, Xuzhou, 221116 ,CN
  • Meng Zhang
     Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China; School of Mines; China University of Mining & Technology, Xuzhou, 221116 ,CN
  • Kun Zhang
     Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China; School of Mines; China University of Mining & Technology, Xuzhou, 221116, ,CN
  • Huiqin Wang
     Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China; School of Mines; China University of Mining & Technology, Xuzhou, 221116, ,CN
  • Aoxiang Liang
     Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China; School of Mines; China University of Mining & Technology, Xuzhou, 221116, ,CN

Keywords:

BP neural network; periodic weighting; pressure prediction; roof control

Abstract

Based on the theory of BP neural network, the monitored data of the cyclic end resistance of hydraulic support in 02178 working face of Huopu mine is trained. Through analyzing the errors produced by the different nodes of network hidden layer, the periodic weighting prediction model whose network structure is 4-12-1 is built. After field monitoring, the 5th weighting is predicted. And the results showed that the average periodic weighting step is 9.1 m, and the influence range covers 1.53 m, and the average dynamic load coefficient is 1.28. Evidently, the output values of network are basically consistent with the monitored data. Therefore, these predicted data can provide a theoretical basis for supporting design and safety production of roadway with the same conditions.

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Published

2022-10-20

How to Cite

Guo, H., Ji, M., Zhang, M., Zhang, K., Wang, H., & Liang, A. (2022). Application of BP Neural Network in the Prediction of Periodic Weighting. Journal of Mines, Metals and Fuels, 67(2), 51–58. Retrieved from https://informaticsjournals.com/index.php/jmmf/article/view/31467

Issue

Volume 67, Issue 2, February 2019

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

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