B. Davvaz, Dian Winda Setyawati, Soleha, I. Mukhlash, Subiono
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引用次数: 1
摘要
摘要粗糙集理论是一种处理不完全知识的数学方法。近集方法导致具有可测量信息内容的样本对象的集合的划分以及特征选择方法。在本文中，我们应用了Bagirmaz[Appl.AlgebrageEngr.Comm.Comput.，30（4）（2019）285-29]和[Davaz et al.，环中的近似。AAECC（2020）的先前结果。https://doi.org/10.1007/s00200-020-00421-3]模块理论。我们引入了环上模中近似近似的概念，这是[B.Davivaz和M.Mahdavipour，Roughness in modules，Information Sciences，176（2006）3658-3674]中提出的模中近似的扩展概念。然后，我们定义了下近子模和上近子模，并研究了它们的性质。
Abstract Rough set theory is a mathematical approach to imperfect knowledge. The near set approach leads to partitions of ensembles of sample objects with measurable information content and an approach to feature selection. In this paper, we apply the previous results of Bagirmaz [Appl. Algebra Engrg. Comm. Comput., 30(4) (2019) 285-29] and [Davvaz et al., Near approximations in rings. AAECC (2020). https://doi.org/10.1007/s00200-020-00421-3] to module theory. We introduce the notion of near approximations in a module over a ring, which is an extended notion of a rough approximations in a module presented in [B. Davvaz and M. Mahdavipour, Roughness in modules, Information Sciences, 176 (2006) 3658-3674]. Then we define the lower and upper near submodules and investigate their properties.
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