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Gold Science and Technology ›› 2015, Vol. 23 ›› Issue (5): 66-71.doi: 10.11872/j.issn.1005-2518.2015.05.066

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Invalidation Risk Evaluation of Backfill Pipe Based on PCA and BP Neural Network

GUO Jiang，ZHANG Bixiao   

  1. School of Resources and Safety Engineering，Central South University，Changsha     410083，Hunan，China
  • Received:2015-04-22 Revised:2015-06-18 Online:2015-10-28 Published:2015-12-09

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Abstract:

For the sake of covering the shortages for neural network in risk evaluation and eliminating human error and subjective grounds，principal component analysis in statistic and BP neural network were combined and used to constructing the invalidate risk evaluation model of backfill pipe，which couple with a large amount of relative data of mine’s backfill pipe system.The investigations found that the input dimension of neural network were reduced and the relationship of all the indexes were also eliminated through dealing with the original data by means of PCA method，and the contrast of optimized BP neural network and standard BP neural network without principal components analysis turned out the former has outstanding merits of rapid analysis and high accuracy in predicting，meanwhile，the rationality of the model was certified according to the results from simulation test.

Key words: Principal Component Analysis（PCA）, BP neural network, invalidate risk evaluation, backfill pipe system

CLC Number: 

  • TD853

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