PM_(2.5) forecasting technology can provide a scientific and effective way to assist environmental governance and protect public health.To forecast PM_(2.5),an enhanced hybrid ensemble deep learning model is proposed ...PM_(2.5) forecasting technology can provide a scientific and effective way to assist environmental governance and protect public health.To forecast PM_(2.5),an enhanced hybrid ensemble deep learning model is proposed in this research.The whole framework of the proposed model can be generalized as follows:the original PM_(2.5) series is decomposed into 8 sub-series with different frequency characteristics by variational mode decomposition(VMD);the long short-term memory(LSTM)network,echo state network(ESN),and temporal convolutional network(TCN)are applied for parallel forecasting for 8 different frequency PM_(2.5) sub-series;the gradient boosting decision tree(GBDT)is applied to assemble and reconstruct the forecasting results of LSTM,ESN and TCN.By comparing the forecasting data of the models over 3 PM_(2.5) series collected from Shenyang,Changsha and Shenzhen,the conclusions can be drawn that GBDT is a more effective method to integrate the forecasting result than traditional heuristic algorithms;MAE values of the proposed model on 3 PM_(2.5) series are 1.587,1.718 and 1.327μg/m3,respectively and the proposed model achieves more accurate results for all experiments than sixteen alternative forecasting models which contain three state-of-the-art models.展开更多
【目的】旨在评估集成学习神经网络模型对落叶松木质材料本构关系的拟合能力和泛化能力,为优化木质产品加工成型提出一种高效率、高精度预测材料受力变形关系的新技术方法。【方法】以落叶松木材为研究对象,结合顺纹压缩试验的标准,前...【目的】旨在评估集成学习神经网络模型对落叶松木质材料本构关系的拟合能力和泛化能力,为优化木质产品加工成型提出一种高效率、高精度预测材料受力变形关系的新技术方法。【方法】以落叶松木材为研究对象,结合顺纹压缩试验的标准,前期对试件的表征和湿度进行了处理,对落叶松木质材料5组试件进行单轴压缩试验,将试验数据作为数据源,在对其进行去噪、聚类、归一等处理的基础上,对12600组数据进行特征提取,建立了学习知识库。利用改进的CLIQUE(Clustering in QUEst)算法对知识库中的样本进行聚类分析,结合局部优化原理和集成学习组合优化理论,构建出一种基于集成学习神经网络的落叶松木质材料受压本构关系模型;然后对关系模型进行训练、学习和仿真,使模型的参数得到优化确认。【结果】1)相比于理论模型,基于集成学习神经网络的落叶松木质材料受压本构关系模型能够与试验数据之间取得更好的吻合效果,说明该模型适合用来描述落叶松木材顺纹受压的力学行为;2)基于集成学习神经网络模型学习速度快,拟合精度高,泛化能力强,可作为材料非线性本构关系的研究模型;3)从试验曲线来看,落叶松材料的抗压强度和弹性模量的变化规律符合弹性力学应力状态分析结论,可以根据本研究提出的模型来预测顺纹方向压力作用下落叶松材料的非线性应力-应变关系;4)预测结果与试验测试存在一定的偏差,可能来自试件的离散性、试件的加工误差和模型学习样本偏少等方面原因。【结论】该模型具有很高的拟合精度和较强的预测能力,可为研究木材本构关系提供一种技术与方法上的参考,对落叶松木材加工成型也有一定的指导意义。展开更多
基金Project(52072412)supported by the National Natural Science Foundation of ChinaProject(2019CX005)supported by the Innovation Driven Project of the Central South University,China。
文摘PM_(2.5) forecasting technology can provide a scientific and effective way to assist environmental governance and protect public health.To forecast PM_(2.5),an enhanced hybrid ensemble deep learning model is proposed in this research.The whole framework of the proposed model can be generalized as follows:the original PM_(2.5) series is decomposed into 8 sub-series with different frequency characteristics by variational mode decomposition(VMD);the long short-term memory(LSTM)network,echo state network(ESN),and temporal convolutional network(TCN)are applied for parallel forecasting for 8 different frequency PM_(2.5) sub-series;the gradient boosting decision tree(GBDT)is applied to assemble and reconstruct the forecasting results of LSTM,ESN and TCN.By comparing the forecasting data of the models over 3 PM_(2.5) series collected from Shenyang,Changsha and Shenzhen,the conclusions can be drawn that GBDT is a more effective method to integrate the forecasting result than traditional heuristic algorithms;MAE values of the proposed model on 3 PM_(2.5) series are 1.587,1.718 and 1.327μg/m3,respectively and the proposed model achieves more accurate results for all experiments than sixteen alternative forecasting models which contain three state-of-the-art models.
文摘【目的】旨在评估集成学习神经网络模型对落叶松木质材料本构关系的拟合能力和泛化能力,为优化木质产品加工成型提出一种高效率、高精度预测材料受力变形关系的新技术方法。【方法】以落叶松木材为研究对象,结合顺纹压缩试验的标准,前期对试件的表征和湿度进行了处理,对落叶松木质材料5组试件进行单轴压缩试验,将试验数据作为数据源,在对其进行去噪、聚类、归一等处理的基础上,对12600组数据进行特征提取,建立了学习知识库。利用改进的CLIQUE(Clustering in QUEst)算法对知识库中的样本进行聚类分析,结合局部优化原理和集成学习组合优化理论,构建出一种基于集成学习神经网络的落叶松木质材料受压本构关系模型;然后对关系模型进行训练、学习和仿真,使模型的参数得到优化确认。【结果】1)相比于理论模型,基于集成学习神经网络的落叶松木质材料受压本构关系模型能够与试验数据之间取得更好的吻合效果,说明该模型适合用来描述落叶松木材顺纹受压的力学行为;2)基于集成学习神经网络模型学习速度快,拟合精度高,泛化能力强,可作为材料非线性本构关系的研究模型;3)从试验曲线来看,落叶松材料的抗压强度和弹性模量的变化规律符合弹性力学应力状态分析结论,可以根据本研究提出的模型来预测顺纹方向压力作用下落叶松材料的非线性应力-应变关系;4)预测结果与试验测试存在一定的偏差,可能来自试件的离散性、试件的加工误差和模型学习样本偏少等方面原因。【结论】该模型具有很高的拟合精度和较强的预测能力,可为研究木材本构关系提供一种技术与方法上的参考,对落叶松木材加工成型也有一定的指导意义。
