基于PSO-RBF神经网络模型的岩爆倾向性预测
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李任豪,顾合龙,李夕兵,侯奎奎,朱明德,王玺
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A PSO-RBF Neural Network Model for Rockburst Tendency Prediction
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Renhao LI,Helong GU,Xibing LI,Kuikui HOU,Deming ZHU,Xi WANG
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表3 岩爆倾向性预测结果
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Table 3 Prediction results of rockburst tendency
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样本序号 | 岩爆指标 | 输出特征值 | PSO-RBF预测等级 | 实际岩爆等级 | RBF预测等级 | Hoek岩爆判据 |
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| / | / | Weq |
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1 | 164 | 0.64 | 2.80 | 8.41 | 4.1671 | Ⅳ | Ⅳ | Ⅲ* | Ⅲ~Ⅳ | 2 | 146 | 0.58 | 6.22 | 5.13 | 2.6765 | Ⅱ~Ⅲ | Ⅲ | Ⅲ | Ⅲ | 3 | 132 | 0.30 | 21.39 | 4.22 | 0.9672 | Ⅰ | Ⅰ | Ⅰ | Ⅰ | 4 | 149 | 0.55 | 6.09 | 5.60 | 3.2167 | Ⅲ | Ⅲ | Ⅲ | Ⅲ | 5 | 139 | 0.4 | 14.8 | 5.38 | 2.0910 | Ⅱ | Ⅱ | Ⅱ | Ⅱ | 6 | 141 | 0.43 | 10.76 | 4.87 | 1.9851 | Ⅱ | Ⅱ | Ⅲ* | Ⅱ | 7 | 152 | 0.57 | 3.71 | 7.26 | 3.7762 | Ⅳ* | Ⅲ | Ⅲ | Ⅲ | 8 | 135 | 0.38 | 16.92 | 4.08 | 1.1662 | Ⅰ | Ⅰ | Ⅰ | Ⅱ* | 9 | 161 | 0.69 | 2.97 | 7.09 | 3.0102 | Ⅲ | Ⅲ | Ⅲ | Ⅳ* | 10 | 130 | 0.31 | 29.86 | 3.96 | 1.1537 | Ⅰ | Ⅰ | Ⅰ | Ⅰ |
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