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Gold Science and Technology ›› 2016, Vol. 24 ›› Issue (3): 64-69.doi: 10.11872/j.issn.1005-2518.2016.03.064

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Strength Prediction of Filling Body Based on PCA and BP Neural Networks

SHI Xiuzhi，FAN Yuqian，SHANG Xueyi   

  1. School of Resources and Safety Engineering，Central South University，Changsha   410083，Hunan，China
  • Received:2015-10-26 Revised:2016-02-26 Online:2016-06-28 Published:2016-10-08

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Abstract:

The strength prediction of filling body is of significant importance in the design of mine filling.The cement to sand ration and the contents of cement，lime，gypsum and slag are selected as influence indexes of the strength of filling body.In addition，the PCA-BP model to predict strength of filling body was constructed by taking advantages that the principal component analysis （PCA） can eliminate the correlation between independent variables and reduce the input data and the BP neural networks has a good predictability.The PCA has been tested on 18 pieces of experimental data，and the five influencing factors that were eventually reduced into three main factors.Then these three main factors were used as BP neural networks input data.Furthermore，the influence of hidden layer neuron on model training process and prediction accuracy was discussed，and results of PCA-BP method，standard BP method and quadratic polynomial regression method were compared.The results show that the optimal structure of PCA-BP method is 3-7-1 and the PCA-BP method is better than the standard BP method and the quadratic polynomial regression method with an error ratio within 3.65%，achieving an accurate prediction for the strength of filling body.In a conclusion，the PCA-BP model provides a high accuracy means for the prediction of the strength of filling body.

Key words: strength of filling body, prediction model, principal component analysis, BP neural networks, cor-relation coefficient

CLC Number: 

  • TD323

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