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首页> 外文期刊>International Journal of Agricultural and Statistical Sciences >APPROACH OF VARIOGRAM MODEL WITH LOGARITHM AND LOGISTIC FUNCTIONS FOR PRE-PROSPEROUS FAMILY
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APPROACH OF VARIOGRAM MODEL WITH LOGARITHM AND LOGISTIC FUNCTIONS FOR PRE-PROSPEROUS FAMILY

机译：具有对数和兴旺家庭对数和逻辑作用的变速仪模型的方法

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摘要

This study offers a variogram model approach in addition to many other theoretical models, based on logarithmic and logistical functions. The development of this model was carried oit following the CNSD rules and variogram behavior both at the originand infinity points, for this reliability model then performed on two Wolfcamp and Pre-prosperous family datasets. The results obtained are that both models work relatively well when compared with two theoretical models of variograms, namely Gauss and Exponentials. The Gaussian Variogram model is used to approximate the variogram experimental model for the wolfcamp data where the gaussian model can be approximated using the logarithmic function. For prosperous pre-family data can be used Exponential Variogram model for experimental approach variogram and exponential variogram can be approached using logistics function. The proposed function has two additional parameters, that is, the base uses base 10 (replaces the number e) and the rank uses a flexible number (recommended pe [0,3]) depending on the data used in the study. The selection of parameters is very important, because each dataset will have different parameters and will not be the same in approaching an experimental variogram. The use of error assessment, &MSE and model selection, AIC is helpful in showing the performance of both functions that are capable of matching even beyond the exponential and gaussian model of work.
机译：除了基于对数和后勤功能的许多其他理论模型之外，本研究还提供了变形仪模型方法。该模型的开发在Orignamd Infinity点的CNSD规则和变速仪行为之后进行了oIT，对于这种可靠性模型，然后在两个Wolfcamp和Pre-Harlussous的家庭数据集上执行。获得的结果是，与变量克的两种理论模型相比，这两个模型都相对良好地工作，即高斯和指数。高斯变形仪模型用于近似使用对数函数近似高斯模型的Wolfcamp数据的变形仪实验模型。对于繁荣的预处理数据，可以使用用于实验方法的指数变速器模型，可以使用物流功能来接近变频器和指数变速器。所提出的函数具有两个附加参数，即基础使用基座10（替换数字e），并且等级使用灵活的数字（推荐的PE [0,3]）取决于研究中使用的数据。参数的选择非常重要，因为每个数据集将具有不同的参数，并且在接近实验变速仪时不会相同。使用错误评估和MSE和模型选择，AIC有助于展示能够匹配的两种功能的性能，即使超出指数和高斯的工作模型。

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