2) Algorithm for extracting Fuzzy rules
模糊规则提取算法
3) Automatic Generation Of Fuzzy Rules
模糊规则自动提取
4) fuzzy rule extraction
模糊规则抽取
5) the extraction of fuzzy rules
模糊规则获取
1.
Since the final membership function value,if obtained by extracting fuzzy rules on high dimension space(face,for instance),is not so reasonable and might very well be zero values,human adjustment is made on the initial value of the membership function during the extraction of fuzzy rules.
对基于模糊神经网络的人脸识别方法进行了研究,提出的模糊神经网络在高维空间(如人脸)上进行模糊规则获取中得到的最终隶属度函数值并不合理,易得到零值的情况,对模糊规则获取过程中的隶属度函数初始值进行人为地调整。
6) rule extraction
规则提取
1.
Raw cotton yarn tenacity's rule extraction based on Rough Set theory;
基于Rough Set理论对原棉纱线强度的规则提取
2.
Data enrichment and rule extraction based on approximate attribute reduction in the context of rough sets;
基于近似属性约简的信息表浓缩和规则提取
3.
Algorithm on rule extraction based on rough set and neural network theory;
基于粗糙集和神经网络理论的规则提取算法
补充资料：模糊规则
模糊规则的形式为：if x is A then y is B
其中A和B为由论域X和Y上的模糊集合定义的语言值。“x is A”称为前提，“y is B”称为结论。
以上模糊规则可以简写为A → B。本质上模糊规则是定义在X × Y上的二元模糊关系R。A → B有
两种解释，一种是A耦合（coupled with）B:
<math>R=A \rightarrow B = A \times B = \int_{X \times Y}^{}tnorm(\mu_A (x), \mu_B (y))/(x,y)</math>
另一种是A导致（entails）B：
<math>R=A \rightarrow B = \bar{A} \cup B</math>
基于以上两种解释和不同的tnorm, tconorm算子，模糊规则可以有多种合法的计算公式。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条