基于ARMA-BP变异系数组合模型的矿山沉陷预计

Mining subsidence prediction based on ARMA-BP variation coefficient combination model

针对单项非线性预测模型预测矿山开采地表沉陷精度低、稳定性差的问题,采用变异系数法组合互补性好的ARMA模型与BP神经网络模型,实验结果:预计值均方根误差为3. 2,平均相对误差0. 11%,结果表明组合模型在精度和稳定性方面具有较大优势。

According to the problems of low accuracy and poor stability of surface subsidence in mining by using the single nonlinear prediction model,variance coefficient method to combine complementary ARMA model and BP neural network model was used.The results showed that the estimated root mean square error was 3.2 and the average relative error was 0.11%.The combined model had the greater advantages in accuracy and stability.