首页> 外文会议>Innovative Computing, Information and Control (ICICIC-2009), 2009 >A New Method of Fuzzy Interpolative Reasoning Based on Gaussian-Type Membership Function
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A New Method of Fuzzy Interpolative Reasoning Based on Gaussian-Type Membership Function

机译:基于高斯型隶属度函数的模糊插值推理新方法

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When rule base is sparse, we cannot get any reasoning result by traditional CRI method for an observation is in the gap between two neighboring antecedents. Ko¿czy and Hirota have proposed a linear interpolative reasoning method, which give a solution for the problem, so fuzzy interpolative reasoning was born. But now, all of the interpolative reasoning methods are almost based on triangular-type membership function, little based on Gaussian-type membership function. Therefore, in this paper, a new method of fuzzy interpolative reasoning based on the proportion of vertex and inflection point of Gaussian-type membership function will be presented, which based on the method of linear interpolative reasoning. It provides a useful tool with fuzzy interpolative reasoning.
机译:当规则库稀疏时,我们无法通过传统的CRI方法获得任何推理结果,因为观察结果位于两个相邻先例之间的间隙中。 Koáczy和Hirota提出了一种线性插值推理方法,该方法为该问题提供了解决方案,因此诞生了模糊插值推理。但是现在,所有插值推理方法几乎都基于三角型隶属度函数,很少基于高斯型隶属度函数。因此,本文提出了一种基于线性插值推理方法的基于高斯型隶属度函数的顶点和拐点比例的模糊插值推理方法。它提供了带有模糊插值推理的有用工具。

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