运用粗集理论对逻辑函数进行知识表达的方法，提出了基于粗糙集的组合逻辑优化方法，并给出了相应的算法。

Rough set-based method of combinatory logic optimization was presented by using knowledge expression of logic function with rough set theory, and its corresponding algorithm was given, also.

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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。

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

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作者从基于分辨矩阵的粗糙集属性约简中受到启发，提出了一系列基于粗集理论的文本特征选择算法，即DB1、DB2、LDB。

The author gains insights from attribute reduction based on discernability matrix and proposes a few rough-set based text feature selection algorithms, i. e. , DB1, DB2 and LDB.

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