变分模态分解与集成增强的残余变形组合预测

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维普

中文摘要：针对非线性、非平稳特征明显的老采空区残余变形数据序列预测结果受模型影响显著的问题,该文提出一种变分模态分解与集成增强的残余变形组合预测方法.利用牛顿插值法获取等间距残余变形数据序列;利用变分模态分解将实验数据分解为扰动项和趋势项;利用自适应提升算法反复训练BP神经网络和长短期记忆神经网络;采用集成增强的BP-AdaBoost模型预测扰动项,LSTM-AdaBoost模型预测趋势项,并等权组合扰动项与趋势项预测结果,完成老采空区残余变形预测.实验结果表明:AdaBoost集成训练能够提升单一模型的预测性能,VMD分解则降低了残余变形数据的非平稳性,组合构建的VMD-BPAda-LSTMAda模型吸收了两种强预测模型各自的优势,削弱了 BP与LSTM模型预测滞后的影响,模型预测性能稳健.

外文标题：Combined prediction of residual deformation in old goaf by variational modal decomposition and integrated enhancement

外文摘要：Aiming at the problem that the prediction results of the residual deformation data series in the old goaf,which has obvious nonlinear and nonsmooth characteristics,are significantly affected by the model,this paper proposes a combined prediction method of residual deformation by variational modal decomposition and integrated enhancement.Firstly,the Newton interpolation method was used to obtain the equally spaced residual deformation data series.Then,the experimental data were decomposed into disturbance and trend terms using variational modal decomposition(VMD).Then the BP neural network and the long short-term memory neural network(LSTM)were trained iteratively using the adaptive boosting algorithm(AdaBoost).Finally,the integrated and enhanced BP-AdaBoost model was used to predict the perturbation term,the LSTM-AdaBoost model predicts the trend term,and the prediction results of the perturbation term and the trend term were combined with equal weights to complete the prediction of the residual deformation in the old goaf.The experimental results show that the AdaBoost integrated training can improve the prediction performance of a single model,while the VMD decomposition reduces the non-stationarity of the residual deformation data,and the combined constructed VMD-BPAda-LSTMAda model absorbs the advantages of each of the two strong prediction models,and weakens the influence of the prediction hysteresis of the BP and LSTM models,which results in the robust model prediction performance.

外文关键词：

old goafresidual deformationcombined predictionvariational mode decompositionadaptive boosting

作者：

薛永安、邹友峰、陈文涛、张文志

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作者单位：

河南理工大学测绘与国土信息工程学院,河南焦作 454000

太原理工大学矿业程学院,太原 030024

河南省采空区场地生态修复与建设技术工程研究中心,河南焦作 454000

山西省水利水电勘测设计研究院有限公司,太原 030024

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关键词：

老采空区残余变形组合预测变分模态分解自适应提升算法

基金：

国家自然科学基金山西煤基低碳联合基金重点项目国家自然科学基金山西煤基低碳联合基金重点项目教育部产学合作协同育人资助项目教育部产学合作协同育人资助项目

项目编号：

U1810203U22A2062020210224500922087106262449

出版年：

2024

DOI：

10.16251/j.cnki.1009-2307.2024.07.002

测绘科学

中国测绘科学研究院

测绘科学

CSTPCD北大核心

影响因子：0.774

ISSN：1009-2307

年,卷(期)：2024.49(7)