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Characterizing Quantifier Fuzzification Mechanisms: A behavioral guide for applications

机译：表征量词模糊化机制：应用行为指南

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摘要

Important advances have been made in the fuzzy quantification field. Nevertheless, some problems remain when we face the decision of selecting the most convenient model for a specific application. In the literature, several desirable adequacy properties have been proposed, but theoretical limits impede quantification models from simultaneously fulfilling every adequacy property that has been defined. Besides, the complexity of model definitions and adequacy properties makes very difficult for real users to understand the particularities of the different models that have been presented. In this work we will present several criteria conceived to help in the process of selecting the most adequate Quantifier Fuzzification Mechanisms for specific practical applications. In addition, some of the best known well-behaved models will be compared against this list of criteria. Based on this analysis, some guidance to choose fuzzy quantification models for practical applications will be provided. (C) 2017 Elsevier B.V. All rights reserved.
机译：在模糊量化领域取得了重要进展。但是，当我们面临针对特定应用选择最方便的模型的决策时，仍然存在一些问题。在文献中，已经提出了几种理想的适当性，但是理论上的限制阻碍了量化模型同时实现已经定义的每个适当性。此外，模型定义和适当性的复杂性使真实用户很难理解已经提出的不同模型的特殊性。在这项工作中，我们将提出几个标准，这些标准可帮助您为特定的实际应用选择最合适的量化器模糊化机制。此外，还将一些最著名的行为良好的模型与此标准列表进行比较。基于此分析，将提供一些为实际应用选择模糊量化模型的指导。 （C）2017 Elsevier B.V.保留所有权利。

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