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A Fuzzy Logic Based Resolution Principal for Approximate Reasoning

机译:近似推理的基于模糊逻辑的分解原理

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In this article, we present a systemic approach toward a fuzzy logic based formalization of an approximate reasoning methodology in a fuzzy resolution, where we derive a truth value of A from both values of B → A and B by some mechanism. For this purpose, we utilize a t-norm fuzzy logic, in which an implication operator is a root of both graduated conjunction and disjunction operators. Furthermore by using an inverse approximate reasoning, we conclude the truth value of A from both values of B → A and B, applying an altogether different mechanism. A current research is utilizing an approximate reasoning methodology, which is based on a similarity relation for a fuzzification, while similarity measure is utilized in fuzzy inference mechanism. This approach is applied to both generalized modus-ponens/modus-tollens syllogisms and is well-illustrated with artificial examples.
机译:在本文中,我们针对模糊解决方案中的近似推理方法,基于模糊逻辑的形式化提出了一种系统方法,其中我们通过某种机制从B→A和B的两个值中得出A的真值。为此,我们使用t范数模糊逻辑,其中蕴涵算符是有级连词和析取算符的根。此外,通过使用逆近似推理,我们使用完全不同的机制从B→A和B的两个值得出A的真值。当前的研究是利用近似推理方法,该方法基于相似关系进行模糊化,而相似度量则用于模糊推理机制。这种方法既适用于广义惯用语/惯用语三段论,又通过人工实例得到了很好的说明。

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