語義網路模型英文怎麼說
語義網路模型 的英文翻譯
語義網路模型 英文 semantic network model 語 : 語動詞[書面語] (告訴) tell; inform 義 : Ⅰ名詞1 (正義) justice; righteousness 2 (情誼) human ties; relationship 3 (意義) meaning; si... 網 : Ⅰ名詞1 (捕魚捉鳥的器具) net 2 (像網的東西) thing which looks like a net 3 (像網一樣的組織或... 路 : 1 (道路) road; way; path 2 (路程) journey; distance 3 (途徑; 門路) way; means 4 (條理) se... 模 : 模名詞1. (模子) mould; pattern; matrix 2. (姓氏) a surname 語義 : semanteme; semantics 網路 : 1. [電學] network; electric network2. (網) meshwork; system; graph (指一維復形); mesh 模型 : 1 (仿製實物) model; pattern 2 (制砂型的工具) mould; pattern3 (模子) model set; mould patter...
語義網路模型 例句
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Chapter two puts forward the general idea about the lexical selection model and then gives a brief introduce to the interlingua and hownet, which will be used in succedent discussions. chapter three introduces the construction of example base and semantics pattern base, which are the main data resources of the lection selection model
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In fact, whichever the silicon valley of usa, or the emillia - romagna of italy, they also have self - feature regional innovation networks, which play an important role in achieving availability configuration of innovation resources and improving innovation ability. this article discusses some questions related to the model dynamism of regional innovation networks and the role of government in the regional innovation networks on the base of description of regional innovation networks. at the same time, this article will put an emphasis on the comparative analysis between the innovation networks of traditional industry district and the innovation networks of high technology industry district, expound how to constitution and develop regional innovation networks. finnaly, this article makes two empirical analysis about regional innovation networks of traditional industry distric of liu shi and software industry district of hang zhou
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Abstract : artifical intelligence methods are implemented to simulate thebehaviors of axially and laterally loaded piles using the field observation tests data obtain ed f rom the drilled shafts and driven piles. the optimal neural network model is deve loped using only simple input data of spt - n values and piles ' geometrical featu r es etc. the analysis for r. c piles of some projects is performed adopting the bp n n and grnn models respectively, and the obtained predicated results are compared w ith the data from conventional design method. it demonstrated the obvious advanta ges of neural networks in the design of pile foundations over the traditional me thods. this paper has an important practical significance and a referential worth iness in the design of pile foundations
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4 ) semantic discovering and matching model based bp network the classifier output is used as training data for a bp neural net. the net produced by this can recognize attributes within the database based on their metadata and emerge learning rules4 )基於bp 網 路 的 語 義 發現和匹配 模 型 設計建立bp 網 路 模 型 ,通過對樣本數據進行學習進而形成匹配規則,用於異構數據庫之間的 語 義 匹配。
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To overcome the difficulty in determining the rbf center numbers and spread, a kind of generalized genetic algorithm is introduced, which follows the analysis of the basic rules of genetic algorithm. the new hybrid algorithm determines the center numbers and spread adaptively to reach the optimal tradeoff between the training accuracy and the generalization, so it increases the prediction accuracy of the model針對建 模 過程中出現的rbf中心和寬度難以確定的難點,在分析遺傳演算法機理和基本演算法的基礎上,提出了使用廣 義 遺傳演算法對rbf神經 網 路 模 型 的中心和寬度進行自適應選擇,從而達到 模 型 訓練精度和范化能力的一個最優的平衡,從而提高 模 型 的預報精度。
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