| 融合混沌残差的BP强预测器的地表下沉预测模型 |
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| 引用本文： | 陈兴达,余学祥,池深深,蒋 创,赵祥硕.融合混沌残差的BP强预测器的地表下沉预测模型[J].大地测量与地球动力学,2020,40(9):913-917. |
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| 作者姓名： | 陈兴达 余学祥 池深深 蒋 创 赵祥硕 |
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| 摘 要： | 为提高地下开采引起地表下沉预测结果的精度，提出融合混沌残差的BP强预测器（BP-Adaboost）的地表下沉预测模型。以顾北矿1312（1）实测值为例，分别用融合混沌残差的BP-Adaboost模型、BP神经网络模型和BP-Adaboost模型对最大下沉值点进行稳定期和活跃期的单步预测和多步预测，结果表明，融合混沌残差的BP-Adaboost模型无论是在单步预测还是在多步预测上的精度均最高，尤其在单步预测上有显著的提高。
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| 关 键 词： | 混沌序列 BP强预测器 BP神经网络 地表下沉预测 残差 |
Surface Subsidence Prediction Model of BP StrongPredictor Fusing Chaos Residuals |
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| CHEN Xingda,YU Xuexiang,CHI Shengsheng,JIANG Chuang,ZHAO Xiangshuo.Surface Subsidence Prediction Model of BP StrongPredictor Fusing Chaos Residuals[J].Journal of Geodesy and Geodynamics,2020,40(9):913-917. |
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| Authors: | CHEN Xingda YU Xuexiang CHI Shengsheng JIANG Chuang ZHAO Xiangshuo |
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| Abstract: | In order to improve the accuracy of the prediction results caused by underground mining, we propose a surface subsidence prediction model of BP-Adaboost, which fuses chaos residuals. Taking the measured value of 1312 (1) of Gubei mine as an example, we use the BP-Adaboost models, the BP neural network model, and BP-Adaboost model fused with chaotic residuals to make one-step and multi-step predictions for the stability and active period of the maximum sinking value point, respectively. The experimental results show that BP-Adaboost model fused with chaotic residuals has the highest accuracy in both one-step prediction and multi-step prediction, especially for one-step prediction. |
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| Keywords: | chaos sequence BP strong predictor BP neural network surface subsidence prediction residual |
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