Compensation for the Error of Narrowing the Defuzzification Range by the Areas’ Ratio Method

N. A. Milostnaya
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Abstract

The purpose of research is to examine the hypothesis that the area ratio method can be used to compensate for the defuzzification interval narrowing error inherent in traditional models, such as center of gravity sums, height models, first maxima, mean maxima, and last maxima.Methods. A fuzzy model consisting of two input variables and one output variable was used to analyze the properties of the area ratio method. The input variables had two triangular membership functions each, and the output variable had three triangular membership functions. The knowledge base consisted of four fuzzy rules. Zadeh's compositional rule was used as an implication model. Two models of classical center of gravity and a model based on the area ratio method were used in the defuzzification process.Results. In the course of experimental studies, it was found that the proposed defuzzifier based on the area ratio method compensates the error of narrowing the defuzzification interval. It was also found during the experimental studies that when using the center-of-gravity model, a resultant surface that only 50% overlaps with the caliper of the output variable is formed at the output, which forms the error of defuzzification interval narrowing. When the area ratio method is used, the resulting surface overlaps 100% with the output variable caliper, suggesting that the area ratio method eliminates the error associated with defuzzification interval narrowing.Conclusion. This article presents a fuzzy MISO model that is used to analyze the properties of the area ratio method. A distinctive feature of the proposed model is the use of the area ratio method in defuzzification. Analysis of its simulation process has shown that this method allows to compensate the error of defuzzification interval narrowing. 
对面积比法缩小去模糊化范围的误差进行补偿
研究的目的是检验面积比方法可以用来补偿传统模型固有的去模糊化区间缩小误差的假设,如重心和模型、高度模型、第一个最大值、平均最大值和最后一个最大值。利用一个由两个输入变量和一个输出变量组成的模糊模型分析了面积比法的特性。输入变量各有两个三角隶属函数,输出变量有三个三角隶属函数。知识库由四条模糊规则组成。采用Zadeh的组合规则作为隐含模型。在去模糊化过程中,采用了经典重心模型和基于面积比法的模型。在实验研究过程中,发现基于面积比法的消模糊器补偿了消模糊区间缩小的误差。在实验研究中还发现,当使用重心模型时,在输出处会形成一个与输出变量的卡尺只重叠50%的结果面,从而形成去模糊化区间缩小的误差。当使用面积比法时,得到的曲面与输出变量卡尺重合100%,表明面积比法消除了去模糊化区间缩小带来的误差。本文提出了一个模糊MISO模型,用于分析面积比法的特性。该模型的一个显著特点是在去模糊化中使用了面积比方法。仿真过程分析表明,该方法可以补偿去模糊化区间变窄的误差。
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