Why Cauchy Membership Functions: Reliability

Javier Viaña, Stephan Ralescu, Kelly Cohen, V. Kreinovich, A. Ralescu
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引用次数: 2

Abstract

An important step in designing a fuzzy system is the elicitation of the membership functions for the fuzzy sets used. Often the membership functions are obtained from data in a traininglike manner. They are expected to match or be at least compatible with those obtained from experts knowledgeable of the domain and the problem being addressed. In cases when neither are possible, e.g., insufficient data or unavailability of experts, we are faced with the question of hypothesizing the membership function. We have previously argued in favor of Cauchy membership functions (thus named because their expression is similar to that of the Cauchy distributions) and supported this choice from the point of view of efficiency of training. This paper looks at the same family of membership functions from the point of view of reliability
为什么柯西隶属函数:可靠性
设计模糊系统的一个重要步骤是得到所使用的模糊集的隶属函数。隶属度函数通常以类似训练的方式从数据中获得。期望它们与从熟悉该领域和正在处理的问题的专家那里获得的结果相匹配或至少兼容。在两者都不可能的情况下,例如,数据不足或专家不可用,我们面临假设隶属函数的问题。我们之前支持柯西隶属函数(这样命名是因为它们的表达式类似于柯西分布),并从训练效率的角度支持这种选择。本文从可靠度的角度研究了同一族隶属函数
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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