In this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, including the selection of a fuzzification and defuzzification method, their implementation in a system dynamics formulations and the validation of the results. We use a physician prescription decision‐making model substructure as an example, and apply the fuzzy rule‐based inference system to determine how a patient is categorized as “low‐risk,” “average‐risk” or “high‐risk.” We emphasize various interpretation challenges and suggest careful selection of the fuzzy operators and defuzzification method, to ensure that the defuzzified values behave reasonably in a dynamic context.

中文翻译：

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系统动力学公式中基于模糊规则的推理

在这项研究中，我们通过建立先前提出的将模糊逻辑与动态建模联系起来的方法，拓宽了系统动力学公式的范围。我们的方法论说明了如何以有意义的方式制定模糊动态变量。我们重点介绍了几个建模挑战，包括选择模糊化和反模糊化方法，在系统动力学公式中实现它们以及验证结果。我们以医师处方决策模型子结构为例，并应用基于模糊规则的推理系统来确定如何将患者分类为“低风险”，“平均风险”或“高风险”。我们强调各种解释挑战，并建议谨慎选择模糊算子和解模糊方法，