融合混沌残差的BP强预测器的地表下沉预测模型

Surface Subsidence Prediction Model of BP Strong Predictor Fusing Chaos Residuals

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。

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.