1) rough membership function
粗隶属函数
1.
We combine the fuzzy set theory with rough set theory by rough membership function and establish a relation between them.
通过粗隶属函数,将粗糙集理论与模糊集理论联系起来,建立一种粗糙集理论与模糊集理论间的关系。
2.
On base of current research work of the quantitative assessment of competency for Chinese seafarers, aiming at the weakness of fuzzy comprehensive assessment, the rough membership function is introduced into the quantitative research of competency for seafarers.
针对综合模糊评价方法的不足,以海船船员适任性定量研究的现状分析为基础,运用粗隶属函数,结合作者选取的海船船员自身条件适任指标体系,对海船船员适任性进行了研究。
2) rough membership function
粗糙隶属函数
1.
The fuzzy set in decision class is transformed into ordinary set by applying a λ-cut,based on which the rough membership function is generalized and some set theoretic properties are discussed.
应用λ-截集将决策类中的模糊集合转换为普通集合,在此基础上推广了粗糙隶属函数,讨论了其中的一些集合理论性质,通过设定置信阈值参数α,提出了一种可以从粗糙模糊决策表中获取概率决策规则的扩展粗糙集方法,并设计了一种改进的快速约简算法,最后给出了该方法的一个算例。
2.
The rough membership function based on the approximation precision ρ__R(X) is defined by the approximation precision of the any rough set on the universe.
利用论域U上任意粗糙集的近似精度ρR(X)定义了基于近似精度的粗糙隶属函数,实现了对任意粗糙集边界域中元素更为准确的刻划。
3.
A rough membership function to make a decision is introduced and a classify algorithm is given.
先通过感知器神经网络训练属性相容权值和相容阈值,再由相容关系确定每个样本的上下近似,通过引入一个用于决策的粗糙隶属函数,给出了分类算法。
3) fuzzy-rough membership function
模糊粗糙隶属函数
1.
For the sake of measuring fuzzy uncertainty and rough uncertainty of real datasets,the fuzzy-rough membership function(FRMF) defined in fuzzy-rough set is introduced.
实际采集的数据中往往存在模糊不确定性和粗糙不确定性,为全面度量数据的不确定性,引入了模糊粗糙集中的模糊粗糙隶属函数概念,并结合容错能力较强的神经网络设计了一种新的模糊粗糙神经网络。
4) subordinate function
隶属函数
1.
The grey number decision making based on TOPSIS and subordinate function;
基于TOPSIS和隶属函数的灰数决策模型
2.
Through establish fuzzy collective of oxide,fix (it's) differentiate reference data from subordinate function,according suitable degree to differentiate acidity and basicity of oxide.
通过对氧化物建立模糊集,按照其隶属函数,确定氧化物酸碱性判别参数,根据贴近度确定氧化物的酸碱性。
3.
This paper applied subordinate function of fuzzy mathemetics to the classfication of rock drillability.
利用模糊数学中的隶属函数对岩石可钻性进行了分级，收到较好效果。
5) Subject function
隶属函数
1.
Fuzzy statistic clustering theory may be applied to establish a subject function taking safety degree of banks as a clustering criterion to classify for quantitative analysis,and Shishou River is taken for an instance.
为定量分析岸坡发生崩岸的危险程度,采用模糊统计聚类理论,建立了以岸坡安全程度为聚类标准的隶属函数,并以石首河段为例对岸坡进行了聚类判别。
2.
This paper analyses the main factors effecting partner selection in dynamic alliance and the ratio distribution of the goal,adopting coherence matrix,fuzzy subject function and genetic algorithm in analytic hierarchy process (AHP) to solve the problem of partner selection in dynamic alliance,and tests the feasibility and validity of the algorithm by carrying on instance emulation.
分析了影响动态联盟伙伴选择的主要因素及目标的权重分配,采用层次分析法中的一致性矩阵、模糊隶属函数与遗传算法结合来解决动态联盟中伙伴选择问题,并进行了实例仿真,表明了算法的可行性和有效性。
3.
The theory of the fuzzy pattern recognition is described and the subject function with adjustable precision is studied in this paper.
文中阐述了旋转机械故障诊断模糊模式识别的原理,研究了诊断精度可调的隶属函数,构建了一个自适应扩充的诊断系统;测试结果表明,此模糊模式识别系统能够高精度地诊断出样本库中存在的故障类别,可用于旋转机械工况的实时监测和诊断场合。
6) membership functions
隶属函数
1.
A method of establishing multivariant membership functions is offered and three such functions are founded, which are employed in the classification of 47 coalfaces in good results.
介绍了一种构造多元隶属函数的方法。
2.
The fuzzy probability of two typical structures in geomechanics engineering,underground diaphragm wall and retaining wall,is calculated by three representational membership functions and a probability density function for the performance function.
目前岩土工程中模糊可靠度计算时隶属函数的选取还没有一个统一标准,针对这种现状进行研究,选取了三种具有代表性的隶属函数,并将其分别应用于岩土工程中的两种典型结构:地下连续墙和挡土墙,通过模糊可靠度的计算以及比较分析,得出如何选取优化隶属函数的有益结论,对工程应用具有实际的指导意义。
3.
Fuzzy set theory is combined with high-order BP neural networks in this paper,and the structure and characteristic of high-order fuzzy BP neural networks and its two-orer algorithm and membership functions are introduced.
将模糊逻辑理论与高阶BP神经网络结合起来,讨论了高阶模糊BP神经网络的结构、特点、二阶算法以及隶属函数的确定。
补充资料：隶属函数
| 隶属函数 membership function 用于表征模糊集合的数学工具。对于普通集合A，它可以理解为某个论域U上的一个子集。为了描述论域U中任一元素u是否属于集合A，通常可以用0或1标志。用0表示u不属于A，而用1表示属于A ，从而得到了U上的一个二值函数χA（u），它表征了U的元素u对普通集合的从属关系，通常称为A的特征函数，为了描述元素u对U上的一个模糊集合的隶属关系，由于这种关系的不分明性，它将用从区间[0，1］中所取的数值代替0，1这两值来描述，记为  （u），数值 （u）表示元素隶属于模糊集 的程度，论域U上的函数μ即为模糊集 的隶属函数，而 （u）即为u对的隶属度。 |
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