Improvement of Membership Function Identification Method in Usability and Precision
Improvement of Membership Function Identification Method in Usability and Precision
复制标题
隶属函数辨识方法可用性和精度的改进
DOI：
10.1007/978-1-4471-0819-1_18
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
A. Yoshikawa
中科院分区：
文献类型：
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作者：
A. Yoshikawa
模糊集在各个领域都有应用。对于模糊集理论知识不多的人来说，提高隶属函数辨识方法的可用性是非常重要的。本文的目的是提出一种新的方法，提高了可用性和精度，然后通过实验来验证该方法。首先，我们提出了识别梯形隶属函数的方法，即边界渐近估计法。该方法的特点是对隶属度进行三元评分，对1-水平集和支撑集的边界进行渐近估计，并利用计算机进行有效的递归选择。心理学实验的结果表明，无论是在对模糊集理论的无经验用户的可用性方面，还是在精确度方面，BASE方法都优于计算机化的模糊图形评定量表上级。
Fuzzy sets are used in various fields. For those who have little knowledge on fuzzy set theory we find that improving usability of membership function identification method is really important. This paper aims to propose this new method improved in usability and precision, and then to verify the method through experiments. First, we propose the method to identify a trapezoidal membership function, BASE method (boundary asymptotic estimation method). The features of this method are ternary rating of membership grades, asymptotic estimation of boundaries of 1-level set and support set, and effective and recursive selection of elements using computers. Results from psychological experiments are showing that the BASE method is superior to the computerized fuzzy graphic rating scale both in usability for inexperienced users of fuzzy set theory and precision.