In this paper, we combine the fuzzy set theory with rough set theory by rough membership function and establish a relation between them.

通过粗糙隶属度函数，将粗集理论与模糊理论联系起来，建立一种粗集理论与模糊理论的关系。

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We combine the fuzzy set theory with rough set theory by rough membership function and establish a relation between them.

通过粗隶属函数，将粗糙集理论与模糊集理论联系起来，建立一种粗糙集理论与模糊集理论间的关系。

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In prediction and modeling, most the responses were found to be best trained using Gaussian input membership functions with a linear output function.

在预测和建模中，通过大量研究发现，最好使用高斯输入函数和线性输出函数。

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The fuzzy linear programming (FLP) problem with nonlinear membership function (NLMF) is usually a nonlinear programming (NLP) problem.

带有非线性隶属函数(NLMF)的模糊线性规划(FLP)问题。通常是一个非线性规划(NLP)问题。

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The fuzzy linear programming (FLP) problem with nonlinear membership function (NLMF) is usually a nonlinear programming (NLP) problem.

带有非线性隶属函数(NLMF)的模糊线性规划(FLP)问题。通常是一个非线性规划(NLP)问题。

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