1) anti-fuzzy centralizer
反模糊中心化子
1.
Using the~1λ-Threshold,some definitions of the anti-fuzzy subgroups,anti-fuzzy normal subgroups,anti-fuzzy normalize and anti-fuzzy centralizer are introduced.
利用1λ-截集引入了反模糊子群、反模糊正规子群、反模糊正规化子及反模糊中心化子的概念,并讨论了它们的性质。
2.
Using the λ-Threshold,some definitions of the anti-fuzzy subgroups,anti-fuzzy normal subgroups,anti-fuzzy normalize and anti-fuzzy centralizer are introduced.
本文利用λ-截集引入了反模糊子群、反模糊正规子群、反模糊正规化子及反模糊中心化子的概念,并讨论了它们的性质。
3.
Based on the definitions and some properties of the anti-fuzzy subgroups and anti-fuzzy normal subgroups,the definitions of anti-fuzzy normalize,anti-fuzzy centralizer,anti-fuzzy conjugate subgroups are introduced and some properties are discussed.
基于已有反模糊子群及反模糊正规子群的概念及性质,给出了反模糊正规化子,反模糊中心化子,共扼子群的概念并讨论了它们的性质,最后讨论了生成反模糊子群。
2) anti-fuzzy normalize
反模糊正规化子
1.
Using the~1λ-Threshold,some definitions of the anti-fuzzy subgroups,anti-fuzzy normal subgroups,anti-fuzzy normalize and anti-fuzzy centralizer are introduced.
利用1λ-截集引入了反模糊子群、反模糊正规子群、反模糊正规化子及反模糊中心化子的概念,并讨论了它们的性质。
2.
Using the λ-Threshold,some definitions of the anti-fuzzy subgroups,anti-fuzzy normal subgroups,anti-fuzzy normalize and anti-fuzzy centralizer are introduced.
本文利用λ-截集引入了反模糊子群、反模糊正规子群、反模糊正规化子及反模糊中心化子的概念,并讨论了它们的性质。
3.
Based on the definitions and some properties of the anti-fuzzy subgroups and anti-fuzzy normal subgroups,the definitions of anti-fuzzy normalize,anti-fuzzy centralizer,anti-fuzzy conjugate subgroups are introduced and some properties are discussed.
基于已有反模糊子群及反模糊正规子群的概念及性质,给出了反模糊正规化子,反模糊中心化子,共扼子群的概念并讨论了它们的性质,最后讨论了生成反模糊子群。
3) fuzzy center
模糊中心
1.
Unitunit reflection is proposed, and uses fuzzyneural network to confirm fuzzy center, discern fuzzy rule and adjust parameters of membership function.
论述了一种神经网络模糊控制器的合成方法 ,提出了单元单元映象方法及利用模糊神经网络确定模糊中心 ,识别模糊规则 ,调节隶属函数参数。
4) defuzzification
反模糊化
1.
A new defuzzification method which is called General dot attenuation (GDA) is presented here.
同时将削减因子的可行域予以适当扩展,提出了广义削减因子反模糊化方法(GDA)。
2.
Based on this idea, such algorithms as similarity measure, defuzzification algorithm and approximate reasoning method are presented.
在分析Vague集性能的基础上 ,提出了Vague集的一种新的三维表示方法 ,用空间曲线来表示Vague集的隶属函数 ,能更直观地反映Vague集所包含的模糊信息 ,在此基础上给出了该Vague集的相似度量算法 ,反模糊化算法以及近似推理算法 。
5) bicentralizer of a module
模的双中心化子
6) anti-fuzzy subgroups
反模糊子群
1.
~1λ-Threshold and anti-fuzzy subgroups;
~1λ-截集与反模糊子群
2.
The definitions and some properties of the anti-fuzzy subgroups and anti-fuzzy normal subgroups are given in paper[1].
文[1]给出了反模糊子群与反模糊正规子群的定义及性质,本文将给出反模糊子群的反模糊正规子群的定义及性质。
补充资料：中心化子
中心化子
ccntratizer
中心化子{侧翻自.血巴;味盯p叨.川)叩l 环、群或半群R的子集，由和某集合S三R的所有元素都交换的元素组成;S在R中的中心化子，记为C:(S)·Abel群的自同态的否可约于争(irredudble，‘ub-ring)(即不稳定任何真子群的子环)在该群的所有自同态的环中的中心化子是除环(Sch盯引理(S‘五urlemma)).
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