前件推导是定理证明的一种扩展。
密封环壳体具有紧固在一起的前件和后件。
The seal ring shell has a front piece and a back piece secured together.
系统网络中含14个待定的前件和后件参数。
There are 14 undetermined parameters named predictor and consequent.
特征网络用来产生模糊规则的前件，相应于每条规则的适用度。
The context network matches the premises of fuzzy rules and produces a matching factor as outputs for each rules.
前件进一步包括连接至足部并径向定位在足部外的径向延伸的胫部。
The front piece further includes a radially extending shin portion connected to the foot portion and located radially outward of the foot portion.
复合规则通过频繁的原子规则前件项组合和支持度和置信度的估算得到。
The compound rules are generated by combination among the frequent antecedent items of atomic rules and the estimation of support and confidence for compound rules.
在模糊推理过程中，推理前件的微小变化往往会引起推理结果的较大变化。
In the process of fuzzy reasoning, a small variance of input often causes a big variance of output of fuzzy reasoning.
前件包括限定与密封环的环形表面干涉配合式接合的轴向延伸的接合表面的足部。
The front piece includes a foot portion defining an axially extending engagement surface for interference-fit engagement with the annular surface of the seal ring.
模糊逻辑系统的区域分割学习方法采用规则前件为不对称高斯型隶属度函数的模糊逻辑系统。
Fuzzy rule region-split method adopts the fuzzy system whose antecedents take the asymmetric Gauss function as membership function.
因为新的项目和一些前件支持定义的类，所以现在您就可以创建一个分析provider类。
With your new project and some up-front support classes defined, you can now create an analysis provider class.
通过改进模糊聚类方法确定模糊模型的前件结构，并对模糊推理关系矩阵进行正交最小二乘估计。
The premise structure of fuzzy model is confirmed by the improved fuzzy clustering, and fuzzy relation matrix of fuzzy model is confirmed by orthogonal least square.
设计一个算法挖掘这样的有趣规刚，它的前件和后件分别属于不同的概念类，称这种规则为类间桥。
This paper designs an algorithm for mining a kind of as- sociation rules that the antecedent and action of such a rule belong to different conceptual classes, referred to class bridges.
假言推理是前提中有一个为假言命题，并且根据假言命题前件与后件之间的关系而推出结论的推理。
Hypothetical reasoning is a reasoning that premise has a proposition and bases on the relation of proposition of protasis and apodosis to made a conclusion.
当模糊产生式规则应用在近似推理过程中时，前件相同而后件不同的模糊规则之间往往存在交互影响。
When fuzzy production rules are used in approximate reasoning, there are interaction influences on rules that have the same consequent but different antecedent.
考虑输入数据与相应直线的接近程度，以及邻近直线对输入数据的影响程度，辨识出了模型的前件参数。
Considering the proximity of input data to relevant linear segment and how the adjacent linear segment affects th.
第一阶段，将模糊分类系统的前件和输入变量编码为一个个体，实现了输入变量论域的动态划分和输入变量选择。
In the first step, the antecedents of fuzzy classification system and input variables are coded into a binary string and treated as an individual in genetic algorithm.
本文通过改进模糊聚类方法确定模糊模型的前件结构，然后对经模糊聚类得到的模糊前件推理矩阵进行QR分解。
In the paper, the premise structure of fuzzy model is obtained by the improved fuzzy clustering. Fuzzy inference matrices are decomposed on the basis of QR decomposition.
首先按过程输出随输入变量变化的程度对输入变量论域进行划分，在此基础上确定模糊模型的规则总数和前件参数；
The domain of discourse of input variables is divided firstly according to the changing degree of the process output while the input variables change.
模糊模型的前件和后件参数分别采用模糊C均值聚类（FCM）和正交最小二乘法（OLS）进行离线或在线辨识。
The T-S fuzzy model's parameters are identified by methods of fuzzy C mean(FCM) and orthogonal least-squares(OLS) online or otherwise.
讨论在条件句前件中“居然”、“竟然”的语法意义，发现以前将它们的语法意义概括为“对已然事实的出乎意料”并不全面。
When we discuss the grammatical meanings of Actually and Unexpectedly, we found that it is not comprehensive to define their grammatical meanings as the unexpected out of the ever known things.
讨论在条件句前件中“居然”、“竟然”的语法意义，发现以前将它们的语法意义概括为“对已然事实的出乎意料”并不全面。
When we discuss the grammatical meanings of Actually and Unexpectedly, we found that it is not comprehensive to define their grammatical meanings as the unexpected out of the ever known things.
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