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An alternative approach to fuzzy control charts: Direct fuzzy approach

机译:模糊控制图的另一种方法:直接模糊法

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The major contribution of fuzzy set theory lies in its capability of representing vague data. Fuzzy logic offers a systematic base to deal with situations, which are ambiguous or not well defined. In the literature, there exist few papers on fuzzy control charts, which use defuzziffication methods in the early steps of their algorithms. The use of defuzziffication methods in the early steps of the algorithm makes it too similar to the classical analysis. Linguistic data in those works are transformed into numeric values before control limits are calculated. Thus both control limits as well as sample values become numeric. In this paper, some contributions to fuzzy control charts based on fuzzy transformation methods are made by the use of a-cut to provide the ability of determining the tightness of the inspection: the higher the value of a the tighter inspection. A new alternative approach "Direct Fuzzy Approach (DFA)" is also developed in this paper. In contrast to the existing fuzzy control charts, the proposed approach is quite different in the sense it does not require the use of the defuzziffication. This prevents the loss of information included by the samples. It directly compares the linguistic data in fuzzy space without making any transformation. We use some numeric examples to illustrate the performance of the method and interpret its results. (c) 2006 Elsevier Inc. All rights reserved.
机译:模糊集理论的主要贡献在于其表示模糊数据的能力。模糊逻辑为处理模棱两可或未明确定义的情况提供了系统的基础。在文献中,关于模糊控制图的论文很少,在其算法的早期阶段就使用了去模糊化方法。在算法的早期步骤中使用去模糊化方法使其与经典分析过于相似。在计算控制极限之前,这些作品中的语言数据将转换为数值。因此,控制极限和样本值都变为数字。在本文中,通过使用a割来为基于模糊变换方法的模糊控制图做出一些贡献,以提供确定检查紧密度的能力:严格检查的值越高。本文还开发了一种新的替代方法“直接模糊方法(DFA)”。与现有的模糊控制图相比,所提出的方法在不需要使用去模糊化的意义上是完全不同的。这样可以防止丢失样本中包含的信息。它可以直接比较模糊空间中的语言数据,而无需进行任何转换。我们使用一些数值示例来说明该方法的性能并解释其结果。 (c)2006 Elsevier Inc.保留所有权利。

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