Unification of imprecise data - translation of fuzzy to multi-valued knowledge over Y-axis

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Abstract

Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.
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不精确数据的统一——y轴上模糊知识到多值知识的转换
推理系统是从基于知识的系统派生出来的定义良好的技术。它们的主要目的是建模和管理知识以及专家推理,以确保在接近人类归纳的同时做出相关决策。虽然被处理的知识通常是不完美的,但它们可以用非经典逻辑来处理,如模糊逻辑或符号多值逻辑。然而,有时需要在同一知识系统中同时考虑模糊多值知识和符号多值知识。为此,本文提出了一种能够对模糊和符号多值知识进行标准化的方法。我们打算通过在其隶属函数的y轴上投影模糊知识来将它们转换为符号类型。因此,在符号多值环境下工作是可行的。我们的方法为专家在建模知识方面提供了更大的灵活性,无论其类型如何。数值研究说明了所提出的方法的潜在应用。
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来源期刊
International Journal of Fuzzy System Applications
International Journal of Fuzzy System Applications Computer Science-Computer Science (all)
CiteScore
2.40
自引率
0.00%
发文量
65
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