implication gate
基本释义:
隐含门
网络释义
1)implication gate,隐含门
2)implication,隐含
3)implicature,隐含
4)connotation,隐含
5)concealment,隐含
6)hidden layer,隐含层
用法和例句
In this paper,the author analyzes the types and cohesive functions of implication,and points out that it is one of the implicit cohesive devices.
本文研究隐含的类型及其实现的意义,探讨隐含的衔接作用,从而说明隐含是隐性衔接的一种手段。
This paper, from the perspective of the structual and semantic categories, discusses the usage of the implication isolated structure of titles.
本文专门探讨篇名中的隐含型孤立结构,主要从结构类型和语义类型两个方面分析这种结构在篇名中的使用情况。
There is implication in the pure scientific text, which is expressed by the means of grammar, vocabulary, semantics, context and pragmatics.
在纯科学篇章中也存在隐含意义 ,具有语法、词汇、语义、语言上下文和语用表达手段。
The Relevance-based Analysis of Implicatures;
以关联理论为基础分析隐含
Metaphor is a kind of weak implicature,and the subjectivity of the translator is apt to be realized in translating metaphor.
隐含可以表达出不同强度的多层意义,使文本呈现出诗学效果。
The theory of conversational implicature was first proposed by Grice in the William James lecture delivered at Harvard.
会话隐含理论最早是由格赖斯于1967年在哈佛大学的讲座中提出的,其基础是合作原则。
On the basis of analyzing the definition of connotation from different perspectives,it points out that the connotation of a word or a language unit is more related to its context than its referent.
本文主要破解了隐含意义的确立与语境密不可分的关系。
on the basis of distinguishing ellipsis and concealment,this thesis discusses the substantival-predicated sentence type which is regarded as "omitting the predicative verb"by some scholars.
在区分省略和隐含的基础上 ,本文讨论了有些学者提出的“省略述语动词”的体词谓语句 ,证明这类体词谓语句中有相当一部分实际上属于隐含范畴 ,并结合认知对这种隐含的语义基础进行了具体分析 ,试图更深入地认识体词谓语
In the semantic research field,whether“Empty category”is concealment or ellipsis attracts peoples great attentions.
在语义研究中,“空语类”是隐含,还是省略,这一课题的研究引起了人们极大的关注。
Determining the number of BP neural network hidden layer units;
BP神经网络隐含层单元数的确定
Determining the number of hidden layer node and analyzing its influence on networks output are important problems in the application of Artificial Neural Networks (ANN).
运用人工神经网络理论和方法 ,建立了水质评价的 B-P网络模型 ;重点探讨了隐含层节点数的确定方法。
In the modeling of Neural Network (NN), an optimization algorithm based on the principle of golden section to design the number of hidden layer nodes is utilized for increasing the precision of risk evaluation.
同时,在B-P神经网络的建模过程中利用一种基于黄金分割原理的优化算法确定隐含层节点数,提高了风险评价的精度。
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