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Estimation of missing values in fuzzy matrices (FM) and interval-valued fuzzy matrices (IVFM)

机译：估计缺失值的模糊矩阵(FM)和区间值模糊矩阵(IVFM)

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摘要

In real-life problems, uncertainty also occurs due to loss of information, which ultimately results in incomplete information. There are many other reasons which also cause incompleteness, e.g., erroneous data measure, insufficient data collection, lack of evidence, etc. To overcome these situations, there are two approaches available: first one, we can ignore the object of missing information and second one, we predict the unavailable data by estimating the missing values. In the present paper, the concepts of fuzzy matrix and interval-valued fuzzy matrix are defined with examples. A new algorithm is proposed to estimate the missing values in fuzzy matrix and its application is illustrated with an example. To generalize the theory of estimation of missing data, another algorithm for interval-valued fuzzy matrix is introduced and applied in a numerical problem. In the end, discussion and comparison are also given with concluding remarks.
机译：在现实生活中的问题,不确定性也会发生丢失的信息,最终的结果在不完整的信息。原因也导致不完整,例如,错误的数据测量、数据不足收集、缺乏证据等。这些情况下,有两种方法可用的:第一,我们可以忽略的对象信息缺失和第二个,我们预测不可用的数据估计丢失的值。模糊矩阵和区间值模糊矩阵定义和例子。提出了在模糊估计缺失值矩阵和说明了该软件的应用的例子。缺失的数据,另一个算法介绍了区间值模糊矩阵应用于一个数值的问题。给出讨论和比较结束语。

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