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A two-dimensional Poisson equation formulation of non-parametric statistical non-linear modeling

机译:非参数统计非线性建模的二维Poisson方程公式

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摘要

The present paper deals with a Poisson equation arising in statistical modeling of semi-deterministic non-linear systems with two independent (input) variables and one dependent (output) variable. Statistical modeling is formulated in terms of a differential equation that relates the second-order joint probability density functions of the model's input/output random variables with the sought non-linear model transference. The discussed modeling procedure makes no prior assumptions on the functional structure of the model, except for monotonicity and continuity with respect to both input variables. In particular, the method is non-parametric. Results of numerical tests are presented and discussed in order to get an insight into the behavior of the devised statistical modeling procedure. The results of numerical tests confirm that the proposed statistical modeling approach is able to cope with both synthetic and real-world data sets and, in particular, with underlying systems and data that exhibit strong hidden nuisance variables and measurement disturbances.
机译:本文研究了在具有两个独立(输入)变量和一个独立(输出)变量的半确定性非线性系统的统计建模中出现的泊松方程。统计模型是根据将模型的输入/输出随机变量的二阶联合概率密度函数与所寻求的非线性模型传递相关联的微分方程来表示的。除了两个输入变量的单调性和连续性之外,讨论的建模过程都没有对模型的功能结构做任何先验假设。特别地,该方法是非参数的。提出并讨论了数值测试的结果,以便深入了解所设计的统计建模程序的行为。数值测试的结果证实了所提出的统计建模方法能够处理合成数据集和实际数据集,尤其是能够处理显示出强大的隐藏干扰变量和测量干扰的基础系统和数据。

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