Surface Subsidence Prediction Model of BP StrongPredictor Fusing Chaos Residuals
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.
Key words ：
chaos sequence
BP strong predictor
BP neural network
surface subsidence prediction
residual
Cite this article:
CHEN Xingda,YU Xuexiang,CHI Shengsheng et al. Surface Subsidence Prediction Model of BP StrongPredictor Fusing Chaos Residuals
[J]. jgg, 2020, 40(9): 913-917.
CHEN Xingda,YU Xuexiang,CHI Shengsheng et al. Surface Subsidence Prediction Model of BP StrongPredictor Fusing Chaos Residuals
[J]. jgg, 2020, 40(9): 913-917.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2020/V40/I9/913
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