基于模糊聚类循环迭代模型的煤仓沉降预测应用研究
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摘要
针对神经网络等模型在时间、荷载等综合因素影响下预测精度不高的问题,提出将模糊聚类循环迭代模型应用于沉降预报,根据前期经验数据对后期沉降趋势进行模拟,引用平均相对误差、均方根误差分别衡量总体精度和偏差;经验证模型精度优于BP神经网络和支持向量机等3种方法。结果表明:基于模糊聚类循环迭代模型适用于多因素影响下的煤仓沉降预测,新沉降预测模型应用也将为工程设计应用提供更多参考。
Aiming at the prediction accuracy of neural network and other model is not high under the influence of time,load and other comprehensive factors,fuzzy clustering loop iterative model was proposed for coal bunker subsidence prediction. The later subsidence tendency simulated on the basis of preliminary date and average relative error and the root mean square error were used to measure overall accuracy and deviation respectively. It is proved that the model accuracy is better than the three models such as BP neural network and support vector machines. The results show that the fuzzy clustering loop iterative model is suitable for coal bank subsidence prediction under the influence of multiple factors. Also,the application of the new subsidence prediction model will provide more reference for engineering design applications.
Aiming at the prediction accuracy of neural network and other model is not high under the influence of time,load and other comprehensive factors,fuzzy clustering loop iterative model was proposed for coal bunker subsidence prediction. The later subsidence tendency simulated on the basis of preliminary date and average relative error and the root mean square error were used to measure overall accuracy and deviation respectively. It is proved that the model accuracy is better than the three models such as BP neural network and support vector machines. The results show that the fuzzy clustering loop iterative model is suitable for coal bank subsidence prediction under the influence of multiple factors. Also,the application of the new subsidence prediction model will provide more reference for engineering design applications.
引文
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[2]厉东伟,陈冉丽.BP神经网络预测模型在高铁沉降预测中的应用[J].测绘通报,2013(S1):192-196.
[3]Kanayama M,Rohe A,PAASSEN L A V.Using and improving neural network models for ground settlem ent prediction[J].Geotechnical&Geological Engineering,2014,32(3):687-697.
[4]陈伟清.回归分析在建筑物沉降变形分析中的应用[J].测绘科学技术学报,2005,22(4):17-19.
[5]李德江,花向红,李涛,等.基于支持向量机的建筑物沉降预测模型研究[J].测绘工程,2009,18(3):29-31.
[6]赵国忱,苏运强,范良.独立分量分析在煤仓沉降分析中的应用[J].测绘通报,2012(6):16-18.
[7]陈守煜,袁晶瑄,李亚伟.基于可变模糊集的模糊聚类迭代模型及其应用[J].大连理工大学学报,2008,48(6):881-886.
[8]冀鸿兰,卞雪军,徐晶.黄河内蒙古段流凌预报可变模糊聚类循环迭代模型[J].水利水电科技进展,2013,33(4):14-17.
[9]李希灿.模糊数学方法及应用[M].北京:化学工业出版社,2016.
[10]陈守煜.模糊聚类循环迭代理论与模型[J].模糊系统与数学,2004,18(2):57-61.
[11]陈守煜.可变模糊集理论与模型及其应用[M].大连:大连理工大学出版社,2009.
[12]Hathaway R J,Beadek J C.Local convergence of the fuzzy c-means algorithms[J].Pattern Recognition,1986,19(6):477-480.