工矿自动化2018，Vol.44Issue(1)：84-88,5.DOI:10.13272/j.issn.1671-251x.2017050027
DHNN模型在岩爆烈度分级预测中的应用研究
Application research of DHNN model in prediction of classification of rockburst intensity
摘要
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.关键词
煤炭开采/深部地下工程/岩爆烈度/分级预测/弹性能量/岩石脆性系数/离散Hopfield神经网络Key words
coal mining/deep underground engineering/rockburst intensity/classification prediction/elastic energy/rock brittleness coefficient/discrete Hopfield neural network分类
矿业与冶金引用本文复制引用
徐佳,陈俊智,刘晨毓,王佳信,龙刚,李春义..DHNN模型在岩爆烈度分级预测中的应用研究[J].工矿自动化,2018,44(1):84-88,5.基金项目
国家自然科学基金资助项目(U1602232). （U1602232）
