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    唐山, 杨丹, 彭鑫, 钟伟民, 万峰. 一种改进的非线性多变量格兰杰因果检验在污水处理过程参数关系分析中的研究[J]. 华东理工大学学报(自然科学版), 2023, 49(1): 87-97. DOI: 10.14135/j.cnki.1006-3080.20211118002
    引用本文: 唐山, 杨丹, 彭鑫, 钟伟民, 万峰. 一种改进的非线性多变量格兰杰因果检验在污水处理过程参数关系分析中的研究[J]. 华东理工大学学报(自然科学版), 2023, 49(1): 87-97. DOI: 10.14135/j.cnki.1006-3080.20211118002
    TANG Shan, YANG Dan, PENG Xin, ZHONG Weimin, WAN Feng. Research on An Improved Nonlinear Multivariable Granger Causality Test in the Analysis of the Relationship between Parameters of Wastewater Treatment Process[J]. Journal of East China University of Science and Technology, 2023, 49(1): 87-97. DOI: 10.14135/j.cnki.1006-3080.20211118002
    Citation: TANG Shan, YANG Dan, PENG Xin, ZHONG Weimin, WAN Feng. Research on An Improved Nonlinear Multivariable Granger Causality Test in the Analysis of the Relationship between Parameters of Wastewater Treatment Process[J]. Journal of East China University of Science and Technology, 2023, 49(1): 87-97. DOI: 10.14135/j.cnki.1006-3080.20211118002

    一种改进的非线性多变量格兰杰因果检验在污水处理过程参数关系分析中的研究

    Research on An Improved Nonlinear Multivariable Granger Causality Test in the Analysis of the Relationship between Parameters of Wastewater Treatment Process

    • 摘要: 传统线性多变量格兰杰因果检验通过引入条件变量来判断两个变量之间是否存在因果关系,但条件变量的选择往往具有主观性而缺乏对其合理的规则,针对这个问题,提出一种可筛选条件变量的非线性多变量格兰杰因果检验方法。该方法使用支持向量回归构建检验方程以适应非线性条件,通过分析两两变量之间的关系构建初步结构后选择条件变量,并基于所选条件变量再进行非线性多变量格兰杰因果检验;引入两种拓扑结构避免对不产生伪因果问题的真实关系重复检验。在数字仿真和污水处理基准仿真平台上的实验结果表明本文方法能适应非线性条件,检验结果更准确,在计算强度上也有更好的表现。

       

      Abstract: In the traditional linear multivariate Granger causality test, conditional variables are introduced to determine whether the causal relationships exist between every two variables or not. Its disadvantage is that the selection of conditional variables is often subjective and lacks reasonable rules. In order to deal with the problem, this paper proposes an improved nonlinear multivariate Granger causality test method with selecting conditional variables. It uses support vector regression to construct test equations to adapt to nonlinear conditions. After analyzing the relationship between two variables and building a preliminary structure, it selects the conditional variable, and then conducts a nonlinear multivariate Granger causality test based on the selected conditional variable. Two kinds of topological structures are introduced to avoid the repeated inspection on some real relationships that do not produce pseudo causality problems. The experimental results on numerical simulation and wastewater treatment benchmark simulation model show that the proposed method can adapt to nonlinear conditions. After screening the condition variables to reduce the interference of independent variables and introducing topology, this method has more accurate test results and better performance in calculation strength.

       

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