The improved fuzzy variable discourse method is studied. Because the essence of variable discourse of universe is equal to change normalized factors of fuzzy variables, increasing the values of normalized factors for fuzzy input variables can reduce the discourse of universe if the basic discourses keep constants. Moreover it is easier to operate for changing normalized factors than variable discourse.
4研究了模糊变论域思想的改进算法，由于改变模糊控制器变量的论域本质上等价于改变变量的正规化因子，因此如果保持模糊控制器的基本论域不变，那么通过增加模糊控制器输入变量的正规化因子的取值同样可以达到缩小论域的目的，而且改变正规化因子相对于论域变化更加容易操作。
The cloud association rules use the normal compatibility clouds defined in the universe of discourse to replace the partition for the universe of attribute. Because the normal compatibility clouds can provide the even transition between aggregative element and non-aggregative element, can overcome the disadvantage of divisiory boundary which is too rigid.
本文提出的云关联规则，用定义在数量属性论域上的正态隶属云替代对属性论域的划分，由于正态隶属云可以在集合元素和非集合元素之间提供非常平滑的变迁，从而克服划分边界过硬的缺点。
There are many attributions could affect accuracy in fuzzy time series model: define the universe of discourse, partition interval, define fuzzy sets, fuzzify data, establish fuzzy logical relationship and defuzzify forecast data. We combine the reasonable attributions from reference and build one-variable model and Type-2 model. We find the accuracy and robustness are improved. The method is worth further study.
影响模糊时间序列的预测准确度有多种属性，论域界定、区间分隔、模糊集合定义、资料模糊化、模糊关系建立、预测转换、反模糊化函数之选定，都是预测模式中须注意之处；本研究结合以及修改文献中较合理的属性选定，建立单变量以及Type-2模式，用於预测BDI之走势，进而分析本研究模式之准确度以及模式稳定性，发现属性修改可得到良好的结果，值得后续研究。
The universe of discourse of error, error in change and output are given.
模糊控制的准则，输入、输出变量的论域也给出了。
Based on the theoretical model of synergistic learning technology system, this dissertation first analyzes the synergistic learning technology system universe of discourse with formal modeling and proposes to build a synergistic learning platform that supports synergistic learning activity management.
论文以协同学习技术系统的理论模型为理论依据，首先对协同学习技术系统论域进行形式化描述，在此基础上提出构建支持协同学习活动管理的协同学习平台。
Based on this, this paper proposes two new methods of universe of discourse partitioning: One method is based on multi-scale ratios to partition the universe of discourse; Another method is based on the adjustable parameter FCM algorithm to partition the universe of discourse.
基于此，本文提出了两种划分论域的新方法：一种是基于多尺度比率的论域划分方法；另一种是基于可调参的FCM算法的论域划分方法。
The primitive notion in rough set theory is a pair of upper and lower approximation operators induced by the basic structure, an approximation space, consisting of a universe of discourse and a binary relation on it.
粗糙集理论的核心算子是从近似空间导出的一对非数值型算子-上近似算子与下近似算子，这一对近似算子是整个粗糙集理论与应用的基础。
Discourse of Universe Compression Algorithm of Flexible Fuzzy Logic Controller Based on Fuzzy Basis Function Approximation
基于Fuzzy函数逼近的柔性模糊控制器论域压缩算法
The algorithm clusters with support vectors as centers in the discourse universe, then form the fuzzy rules: cluster center as antecedent proposition and linear partial least-squares regression model of the cluster as consequent proposition.
该算法以支持向量为中心在论域空间模糊聚类，然后根据聚类结果形成模糊规则：模糊规则的前件为聚类中心，后件为对应该类的的线性偏最小二乘回归模型。
A flexible factor was introduced to the fuzzy control model, which can self-adapt and adjust the universe of discourse in the fuzzy control and the workpiece peripheral speed v_w was selected as control variable.
采用论域自调整策略和模糊控制理论建立了纵向磨削的控制模型，选择工件的转速vw作为控制变量。
This paper presents concepts of elementary transfer F, F on the universe of discourse U, single-direction singular set and both-direction singular set.
给出论域U上元素迁移F，F，单向奇异集合，双向奇异集合的概念；
Let Vis the universe of discourse of W, v_j is the observed value of w_j, it is called that W provides the information as measure value in 1 to v_j through w_j.
设W的论域为V，wj的观测值为vj，称W通过wj给V上的vj点提供了一个量值为1的信息。
Studies find universe of discourse partition and data fuzzification are two particularly important parties on the fuzzy time series models.
研究发现模糊时间序列模型中论域划分与数据模糊化是两个重要部分。
This method includes two parts. In the FTS processing part, establish heuristic rules and fuzzy variables and determine the universe of discourse, fuzzy sets and membership degree functions, then fuzzify the history data.
该方法包括两部分：在模糊时序处理部分通过建立启发式规则、模糊变量、论域、模糊集合和隶属度函数，完成历史数据的模糊化；