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首页> 外文期刊>Artificial Intelligence Review: An International Science and Engineering Journal >Novel classes of coverings based multigranulation fuzzy rough sets and corresponding applications to multiple attribute group decision-making
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Novel classes of coverings based multigranulation fuzzy rough sets and corresponding applications to multiple attribute group decision-making

机译：基于多个人模糊粗糙集的新型覆盖物的覆盖类别与多个属性组决策的相应应用

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摘要

The notion of covering based multigranulation fuzzy rough set (CMGFRS) models is a generalization of both granular computing and covering based fuzzy rough sets. Therefore it has become a powerful tool for coping with vague and multigranular information in cognition. In this paper we introduce three kinds of CMGFRS models by means of fuzzy beta-neighborhoods and fuzzy complementary beta-neighborhoods, and we investigate their axiomatic properties. We investigate three respective types of coverings based CMGFRS models, namely, optimistic, pessimistic and variable precision setups. In particular, by using multigranulation fuzzy measure degrees and multigranulation fuzzy complementary measure degrees, we derive three types of coverings based gamma-optimistic (gamma-pessimistic) CMGFRSs and E (F, G)-optimistic and E (F, G)-pessimistic CMGFRSs, respectively. We discuss the interrelationships among these three types of CMGFRS models and covering based Zhan-CMGFRS models. In view of the theoretical analysis for these three types of CMGFRS models, we put forward a novel methodology to multiple attribute group decision-making problem with evaluation of fuzzy information. An effective example is fully developed, hence concluding the applicability of the proposed methodology.
机译：覆盖基于多个人模糊粗糙集（CMGFRS）模型的概念是颗粒计算和覆盖基于模糊粗糙集的概念。因此，它已成为应对在认知中的模糊和复杂信息的强大工具。在本文中，我们通过模糊的β-街区和模糊互补β-邻居介绍了三种CMGFRS模型，并研究了它们的公理性质。我们研究了三种基于CMGFRS模型的各种类型的CMGFRS模型，即乐观，悲观和可变精度设置。特别地，通过使用多个人模糊测量度和多元体模糊互补度测量度，我们推导出三种类型的伽马乐观（伽马悲观）CMGFRSS和E（F，G） - 优化和E（F，G） - 自我CMGFRSS分别。我们在这三种类型的CMGFRS模型中讨论了相互关系和基于Zhan-CMGFRS模型。鉴于这三种类型的CMGFRS模型的理论分析，我们向多个属性组决策问题提出了一种新的方法，评估模糊信息。完全开发了一个有效的例子，因此结束了所提出的方法的适用性。

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