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Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data

机译：非线性偏最小二乘用于气象数据一致性分析

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摘要

Considering the different types of error and the nonlinearity of the meteorological measurement, this paper proposes a nonlinear partial least squares method for consistency analysis of meteorological data. For a meteorological element from one automated weather station, the proposed method builds the prediction model based on the corresponding meteorological elements of other surrounding automated weather stations to determine the abnormality of the measured values. For the proposed method, the latent variables of the independent variables and the dependent variables are extracted by the partial least squares (PLS), and then they are, respectively, used as the inputs and outputs of neural network to build the nonlinear internal model of PLS. The proposed method can deal with the limitation of traditional nonlinear PLS whose inner model is the fixed quadratic function or the spline function. Two typical neural networks are used in the proposed method, and they are the back propagation neural network and the adaptive neuro-fuzzy inference system (ANFIS). Moreover, the experiments are performed on the real data from the atmospheric observation equipment operation monitoring system of Shaanxi Province of China. The experimental results verify that the nonlinear PLS with the internal model of ANFIS has higher effectiveness and could realize the consistency analysis of meteorological data correctly.
机译：考虑到不同类型的误差和气象测量的非线性，提出了一种非线性偏最小二乘方法，用于气象数据的一致性分析。对于来自一个自动气象站的气象要素，该方法基于周围其他自动气象站的相应气象要素建立了预测模型，以确定测量值的异常。对于所提出的方法，通过偏最小二乘（PLS）提取自变量和因变量的潜变量，然后分别将它们用作神经网络的输入和输出，以建立非线性的内部模型。 PLS。该方法可以解决内部模型为固定二次函数或样条函数的传统非线性PLS的局限性。该方法使用了两种典型的神经网络，即反向传播神经网络和自适应神经模糊推理系统（ANFIS）。此外，还对来自陕西省大气观测设备运行监测系统的真实数据进行了实验。实验结果证明，采用ANFIS内部模型的非线性PLS具有较高的有效性，可以正确实现气象数据的一致性分析。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第18期|143965.1-143965.8|共8页
  • 作者

    Meng Zhen; Zhang Shichang; Yang Yan; Liu Ming;

  • 作者单位

    Atmospher Observat Tech Support Ctr Shaanxi Prov, Xian 710014, Peoples R China;

    Atmospher Observat Tech Support Ctr Shaanxi Prov, Xian 710014, Peoples R China;

    Atmospher Observat Tech Support Ctr Shaanxi Prov, Xian 710014, Peoples R China;

    Atmospher Observat Tech Support Ctr Shaanxi Prov, Xian 710014, Peoples R China;

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