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A Novel Method of the Generalized Interval-Valued Fuzzy Rough Approximation Operators

机译:广义区间值模糊粗糙近似算子的一种新方法。

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摘要

Rough set theory is a suitable tool for dealing with the imprecision, uncertainty, incompleteness, and vagueness of knowledge. In this paper, new lower and upper approximation operators for generalized fuzzy rough sets are constructed, and their definitions are expanded to the interval-valued environment. Furthermore, the properties of this type of rough sets are analyzed. These operators are shown to be equivalent to the generalized interval fuzzy rough approximation operators introduced by Dubois, which are determined by any interval-valued fuzzy binary relation expressed in a generalized approximation space. Main properties of these operators are discussed under different interval-valued fuzzy binary relations, and the illustrative examples are given to demonstrate the main features of the proposed operators.
机译:粗糙集理论是处理知识的不精确性,不确定性,不完整性和模糊性的合适工具。本文构造了新的广义模糊粗糙集的上下近似算子,并将其定义扩展到区间值环境。此外,分析了这类粗糙集的性质。这些算子显示为等效于Dubois引入的广义区间模糊粗糙近似算子,后者由广义近似空间中表达的任何区间值模糊二元关系确定。在不同的区间值模糊二元关系下讨论了这些算子的主要性质,并通过举例说明了所提出算子的主要特征。

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