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Using qualitative bond graph reasoning to derive look-up tables for fuzzy logic controllers

机译:使用定性绑定图推理来导出用于模糊逻辑控制器的查找表

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Qualitative reasoning, bond graphs and fuzzy logic are developed respectively in three different fields: computer science, system dynamics and automatic control. They all have their own strengths and weaknesses. Fortunately, these methods are compensating and they can be used to offset each other's weaknesses. Qualitative reasoning on bond graphs leads to ambiguities in its reasoning process while these ambiguities can be resolved by self-organising fuzzy logic through employing the system identification approach to articulate models. On the other hand, fuzzy logic treats a system as a black box and does not use any structural information, while qualitative reasoning on bond graphs contains this information and can be used to systematically derive look-up tables in fuzzy logic. The paper presents a method of using structural information to generate look-up tables, which will then be used for fuzzy logic control. An example is analysed to explain the concepts.
机译:三种不同领域分别开发了定性推理,键合图和模糊逻辑:计算机科学,系统动力学和自动控制。他们都有自己的优势和弱点。幸运的是,这些方法正在补偿,它们可用于抵消彼此的弱点。在债券图上的定性推理导致其推理过程中的歧义,而通过使用系统识别方法来表达模型,可以通过自组织模糊逻辑来解决这些歧义。另一方面,模糊逻辑将系统视为黑匣子,不使用任何结构信息,而绑定图上的定性推理包含此信息,并可用于系统地派生模糊逻辑中的查找表。本文介绍了一种使用结构信息生成查找表的方法,然后将用于模糊逻辑控制。分析了一个例子来解释概念。

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