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 ensure 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, the authors propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. The authors 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. The 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.
展开▼
机译：推理系统是一种定义明确的技术，源自基于知识的系统。他们的主要目的是对知识以及专家推理进行建模和管理，以确保在接近人类归纳的同时做出相关的决策。尽管处理的知识通常是不完美的，但可以使用非经典逻辑将其视为模糊逻辑或符号多值逻辑。尽管如此，有时需要在同一基于知识的系统中同时考虑模糊和符号多值知识。为此，作者在本文中提出了一种能够标准化模糊和符号多值知识的方法。作者打算通过将模糊知识投影到其隶属函数的 Y 轴上，将它们转换为符号类型。因此，在符号多值上下文下工作变得可行。这种方法为专家提供了更大的灵活性，可以对他们的知识进行建模，无论他们的类型如何。通过数值分析，对所提方法的潜在应用进行了说明。
展开▼
