针对大坝变形常规统计预报模型在监测信息挖掘时的优势单一性及预报精度欠佳等问题,视大坝变形观测资料为非平稳时间序列,从影响大坝变形的因素出发,将其分为周期性影响因素与随机影响因素,利用多尺度小波分析方法将大坝变形监测序列分...针对大坝变形常规统计预报模型在监测信息挖掘时的优势单一性及预报精度欠佳等问题,视大坝变形观测资料为非平稳时间序列,从影响大坝变形的因素出发,将其分为周期性影响因素与随机影响因素,利用多尺度小波分析方法将大坝变形监测序列分解并重构,结合BP神经网络与自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)对其随机信号与系统信号分项训练预报,并将其预报值相叠加,据此,应用时间序列原理提出了一种基于BP-ARIMA的混凝土坝多尺度变形组合预报模型。工程实例分析表明,所建组合模型较常规模型能够有效挖掘监测信息中所蕴含的有效成分,预报精度显著提升,且计算分析过程简便,为高边坡及水工建筑物中其他监测指标的预报提供了新方法。展开更多
The increasingly mature nonlinear technique can facilitate accurate forecasting of transient sap flow process of plant.In this paper,the dominated tree species,Pinus tabulaeformis and Platycladus orientalis in Beijing...The increasingly mature nonlinear technique can facilitate accurate forecasting of transient sap flow process of plant.In this paper,the dominated tree species,Pinus tabulaeformis and Platycladus orientalis in Beijing mountainous area were chosen for study.Their monitoring data range from June 18 th to September 9 th 2007 was derived to form the 1 985 sets of sample respectively.BP (back propagation) neural network models were established according to the theory of automaton network of discrete dynamic system,the target output of which was sap flow velocity and the inputs of which consisted of five influencing factors,ie,air temperature,relative humidity,light intensity,stem diameter growth and soil water potential.To improve the generalization quality of networks,Bayesian regularization and early stopping modes were involved in the training process.After training in two modes above,the linear regression between simulated outputs and the corresponding targets of test sample sets showed good fits (R>0.85),which indicated a high forecasting precision of the models established,specifically when 11 neurons in hidden layer.Models demonstrated fine generalization under the two training modes in that the fit of test sample was equivalent to that of training sample,which further indicated their availability in practice.展开更多
文摘针对大坝变形常规统计预报模型在监测信息挖掘时的优势单一性及预报精度欠佳等问题,视大坝变形观测资料为非平稳时间序列,从影响大坝变形的因素出发,将其分为周期性影响因素与随机影响因素,利用多尺度小波分析方法将大坝变形监测序列分解并重构,结合BP神经网络与自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)对其随机信号与系统信号分项训练预报,并将其预报值相叠加,据此,应用时间序列原理提出了一种基于BP-ARIMA的混凝土坝多尺度变形组合预报模型。工程实例分析表明,所建组合模型较常规模型能够有效挖掘监测信息中所蕴含的有效成分,预报精度显著提升,且计算分析过程简便,为高边坡及水工建筑物中其他监测指标的预报提供了新方法。
文摘The increasingly mature nonlinear technique can facilitate accurate forecasting of transient sap flow process of plant.In this paper,the dominated tree species,Pinus tabulaeformis and Platycladus orientalis in Beijing mountainous area were chosen for study.Their monitoring data range from June 18 th to September 9 th 2007 was derived to form the 1 985 sets of sample respectively.BP (back propagation) neural network models were established according to the theory of automaton network of discrete dynamic system,the target output of which was sap flow velocity and the inputs of which consisted of five influencing factors,ie,air temperature,relative humidity,light intensity,stem diameter growth and soil water potential.To improve the generalization quality of networks,Bayesian regularization and early stopping modes were involved in the training process.After training in two modes above,the linear regression between simulated outputs and the corresponding targets of test sample sets showed good fits (R>0.85),which indicated a high forecasting precision of the models established,specifically when 11 neurons in hidden layer.Models demonstrated fine generalization under the two training modes in that the fit of test sample was equivalent to that of training sample,which further indicated their availability in practice.
