从单词计算到模糊概念推理(RFC)

Yingxu Wang
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引用次数: 0

摘要

本文正式研究了语言结构和语义的模糊性,提出了一种模糊概念推理方法。在概念代数和语义代数的基础上引入了模糊概念和模糊语义的数学模型。定量分析了模糊修饰语和模糊量词对模糊概念的语义作用。形式概念的集体内涵和外延引出实验表明,人类知识的模糊性源于认知的复杂性、说明性、主观性、多样性、冗余性、不完全性、混合同义词、非正式表征、不连贯属性、发散对象和语境影响。RFC方法为认知机器人、深度机器学习和模糊系统提供了一种正式的词计算(CW)方法,以在广泛的应用中严格操作模糊语言实体、语义和推理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From computing with words (CWW) to reasoning with fuzzy concepts (RFC)
The fuzzy nature of language structures and semantics is formally studied towards a methodology for reasoning with fuzzy concepts (RFC). Mathematical models of fuzzy concepts and fuzzy semantics are introduced based on concept algebra and semantic algebra. The semantic effects of fuzzy modifiers and quantifiers on fuzzy concepts are quantitatively analyzed. Experiments on collective intension and extension elicitation for formal concepts demonstrate that fuzziness of human knowledge stem from the cognitive complexity, inexplicitness, subjectivity, diversity, redundancy, incompleteness, mixed synonyms, informal representation, incoherent attributes, divergent objects, and contextual influence. The RFC methodology provides a formal approach to computing with words (CW) for cognitive robots, deep machine learning, and fuzzy systems to rigorously manipulate fuzzy language entities, semantics, and reasoning in a wide range of applications.
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