融合混沌残差的BP强预测器的地表下沉预测模型

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。

Surface Subsidence Prediction Model of BP StrongPredictor Fusing Chaos Residuals

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.