1) fuzzy control rule base
模糊控制规则库
2) fuzzy rules
模糊控制规则
1.
The controller can learn the fuzzy rules and can adjust the fuzzy rules with the change of system automatically.
该控制器可以自动学习模糊控制规则 ,并随系统的变化自动调节模糊控制规则。
3) fuzzy control rules
模糊控制规则
1.
Combining rough set theory with fuzzy logic technology, this paper has presented a method of gaining fuzzy control rules based on measured data.
将粗糙集理论与模糊逻辑技术相结合,提出了一种通过测量数据来获取模糊控制规则的方法。
2.
The expression of fuzzy logic system is combined with the list of fuzzy control rules in this paper.
提出将模糊逻辑系统表达式与模糊控制规则表相结合,并用S-function将其形成模块,再用Simulink对所设计的模糊控制系统仿真,编程快捷、调试方便。
3.
The paper used COA successfully optimizing the fuzzy control rules parameters for a complex system with nonlinear and time variation.
利用混沌 (chaos)所特有的外在的随机性、遍历性及内在的规律性 ,将其应用于一类复杂非线性时变过程的模糊控制规则优化中 ,提出了一种基于混沌思维的算法来优化模糊规则参数 。
4) fuzzy control rule
模糊控制规则
1.
A solution of non-compatibility problem in generation process of fuzzy control rule;
模糊控制规则生成过程中不相容问题的一种解决方案
2.
Method of simplifying fuzzy control rules based on separated variables;
基于分离变量的模糊控制规则的简化设计
3.
Method of Optimizing Fuzzy Control Rules Based on Compatibility of Fuzzy Subsets;
基于模糊集相容性的模糊控制规则优化方法
5) fuzzy control rulers
模糊控制规则表
1.
This paper describes how to design fuzzy control rulers by genetic algorithm,which resolves the problem of lack expert experiences and promotes the application of fuzzy techniques.
提出一种利用遗传算法对模糊控制规则表进行设计的方法,解决了在实际中设计模糊控制规则表时,经常遇到的缺乏专家经验指导的矛盾,有助于模糊控制技术的推广和应用。
6) fuzzy rule base
模糊规则库
1.
A fuzzy rule base characterizing the relationship between input and output parameters was built by experiments.
采用模糊数学方法建立了清洗率预测的数学模型,通过实验建立了用于预测清洗率的模糊规则库。
2.
The paper optimizes the method of singular value decomposition (SVD) for compressing the fuzzy rule base in fuzzy controllers.
本文将压缩模糊控制器中模糊规则库的方法—奇异值分解(SVD)进行优化,让输入变量乘上比例因子,以防止输入空间信息丢失,从而提高系统的性能。
3.
Firstly, an unsupervised clustering technique is used to determine the number of fuzzy rules and generate an initial fuzzy rule base from the given input output data.
通过一个无监督的聚类算法自组织地确定模糊规则的数目及生成一个初始的模糊规则库 ,构造一类模糊神经网络 ,通过调整网络的权值 ,使规则库中的参数更加精确 。
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