Application research of DHNN model in prediction of classification of rockburst intensity
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摘要: 针对现有岩爆预测方法权重的确定带有随意性和主观性问题，提出了一种岩爆烈度分级预测的离散Hopfield神经网络（DHNN）模型。该模型选取应力系数、岩石脆性系数及弹性能量指数作为评价指标，将岩爆等级分为强岩爆、中等岩爆、弱岩爆及无岩爆4级，然后进行编码，不需要对样本数据进行归一化处理，只需转换成“1”和“-1”的二值型模式，编码简单，网络迭代次数少，具有很好的联想记忆功能，使岩爆烈度分级预测更加科学合理，可为深部地下工程岩爆烈度分级预测提供一种新途径。典型岩爆工程实例预测结果证明了该模型的正确性。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 need'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.
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