多源异构变形预测模型融合方法及其在开采沉陷动态预测中的应用研究
首发时间:2016-12-27
摘要:为了克服单一非线性开采沉陷预测模型预测精度不高、可靠性差的缺点,通过对开采沉陷特点分析和非线性预测模型优缺点比较,优选了四种适应性强、性能互补好的AR模型、GM模型、三次指数平滑法模型和卡尔曼滤波模型,基于模型误差平方和最小的融合准则,构建了适用于开采沉陷动态预测的多源异构变形预测模型。基于建立的通用模型,利用徐州矿区三河尖矿东矿7126工作面地表移动观测站前20期观测值,求取了多源异构融合模型下沉预测模型权系数p1=0.1835、p2=0.2343、p3=0.3497、p4=0.2324,水平移动预测模型权系数p'1=0.1011、p'2=0.1109、p'3=0.4864、p'4=0.2935,并利用21-25期观测值对模型的预测性能进行了检验,结果表明多源异构融合模型相对于这四种单一模型预测精度高、可靠性好。本文研究成果对矿山开采沉陷动态预测具有较好的参考价值。
关键词: AR模型 GM模型 三次指数平滑法模型 卡尔曼滤波模型 多源异构变形预测模型
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Research on fusion method of multi-source heterogeneous deformation prediction model and the application in dynamic prediction of mining subsidence
Abstract:In order to overcome the disadvantage of low precision and poor reliability of the single nonlinear mining subsidence prediction model,by comparing the characteristics of mining subsidence and the advantages and disadvantages of nonlinear prediction model, four models with strong adaptability and complementary performance ,which are AR model, GM model, Cubic exponential smoothing model and Kalman filtering model are selected. Based on the model error square and minimum fusion criterion, a multi-source heterogeneous deformation prediction model is constructed for mining subsidence dynamic prediction. Based on the established general model,the first 20 observation points of the 7126 working surface movement observation station in Sanhejian east mine in Xuzhou mining area were used to calculate the weight coefficients of the multi-source heterogeneous fusion model sinking prediction model are p1 = 0.1835, p2 = 0.2343, p3 = 0.3497, p4 = 0.2324, and the horizontal prediction model are p'1 = 0.1011, p'2 = 0.1109, p'3 = 0.4864, p'4 = 0.2935. And the predictive performance of the model is tested by using the 21-25 period observed values, the results show that the multi-source heterogeneous fusion model has high precision and good reliability compared with these four single models. The results of this paper have good reference value for dynamic prediction of mining subsidence.
Keywords: AR model GM model Cubic exponential smoothing model Kalman filtering model Multi-source heterogeneous deformation prediction model
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