Application research of DHNN model in prediction of classification of rockburst intensity
XU Jia
CHEN Junzhi
LIU Chenyu
WANG Jiaxin
LONG Gang
LI Chunyi
Abstract：In view of problems of randomness and subjectivity in determining weight of existing rockburst prediction methods,a discrete Hopfield neural network (DHNN) model for prediction of classification of rockburst intensity was proposed.The model selects stress coefficient,rockbrittleness coefficient and elastic energy index as evaluation index,divides rockburst grade into 4 stages,such as strong rockburst,medium rockburst,weak rockburst and no rockburst,then encodes them.The model needn't normalize sample data with simpler encoding,lesser iterations of network and better associative memory ability,only be converted to "1" and "-1" of the two value model,therefore,the classification prediction of rockburst intensity is more scientific and reasonable.The model can provide a new way for classification prediction of rockburst intensity in deep underground engineering.The prediction results of typical rockburst engineering examples prove the correctness of the model.
Keywords：coal miningdeep underground engineeringrockburst intensityclassification predictionelastic energyrock brittleness coefficientdiscrete Hopfield neural network
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 84-88 )