神經樹網路 的英文怎麼說

中文拼音 [shénjīngshùwǎnglù]
神經樹網路 英文
ntn neural tree network
  • 神 : Ⅰ名詞1 （神靈） god; deity; divinity 2 （精神; 精力） spirit; mind 3 （神氣; 神情） expression; l...
  • 經 : 經動詞[紡織] （把紡好的紗或線梳整成經紗或經線） warp
  • 樹 : Ⅰ名詞1 （木本植物的通稱） tree 2 （姓氏） a surname Ⅱ動詞1 （種植; 栽培） plant; cultivate 2 （樹...
  • 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...
  • 神經 : nerve; nervus
  • 網路 : 1. [電學] network; electric network2. （網） meshwork; system; graph （指一維復形）; mesh
  1. After analyzing the character of risk, i introduce data mining method into risk management, to solve the contradiction between great capacity of data and lack of information, the methods include mathematics statistics and artificial neural network ( ann ). then, i study on the methods of risk management in risk identification, risk evaluation and risk disposal, what is advanced, fault tree analysis method based on fuzzy probability, stochastic simulation method and the topsis method based on interval number all consider the characteristic of risk. finally, i discussed the application of information system ( mis ) in project risk management, and developed a risk management information system

    論文在深入分析了風險特徵之後，將數據挖掘技術引入風險管理，用以解決海量數據與貧乏信息之間的矛盾，所採用的技術有數理統計和人工神經網路（ ann ）兩種方法；接著，論文對風險識別、風險評價、風險處理中的風險管理方法進行了研究，所提出的基於模糊概率的故障樹技術、隨機模擬技術和基於區間數的topsis方法都體現了風險管理的特點；最後，論文對信息系統（ mis ）在工程項目風險管理中的應用進行了探討，開發出一個風險管理信息系統。
  2. Shenqiang adhesive co., ltd. specializes in producing and marketing powerful adheseves under the trademark " shenqiang " " ivies ". in 2001, the company passed is9002 attestation, in 2005got the certificate of 14001 environmental management system. through unremitting efforts, the company has built the fine product image, the sales network not only spread all over the country, but alsoin the europe, usa etc. the company adheres tothe managing concept of " super quality, sincere service ", we shall constantly offer you with better product and more satisfactory service

    神強粘合劑有限公司主要以生產和銷售強力接著劑系列產品的專業化企業.公司擁有"強" " ivies "等注冊商標， 2001年通過is9002質量體系認證. 2005年初通過14001環境管理體系認證.經過我們不懈努力下，在國內外樹立了良好的產品形象，產品營銷網路已遍及全國，並遠銷歐美等地區.公司秉著"卓越品質，誠信服務"的企業經營理念，我們將一如既往地為您提供更優質的產品和更滿意的服務
  3. So bp neural network is used to classify the stored food insects primarily. the result shows our method is effective

    實驗表明，利用bp神經網路分類器作初級分類器的樹分類結構進行分類，是一種行之有效的方法。
  4. Many different techniques have been proposed for classification, including statistical approaches, neural networks, decision tree algorithm and rough sets

    現有數據分類方法有統計方法、決策樹分類方法、神經網路方法、粗集法等。
  5. This study employed six data mining methods, including logistic regression, discriminant analysis, artificial neural networks, k - nearest - neighbors, na ? ve bayes classifier, and classification trees, to find the most important factors of earthquake - caused landslide

    本研究利用六種資料探勘方法，包括邏輯回歸、判別分析、類神經網路、最近鄰法、貝氏分類器、分類樹，探討影響地震引起山崩的重要因子。
  6. Chapter two : given the data available and this data excavation task, it makes a study method comparison between neural network and judgment - tree. it confirms the latter as the model and presents a brief statement on judgment - tree

    第二章：針對得到的實際數據和本次數據挖掘任務，通過與神經網路法的比較，確定判定樹法為建模方法，同時對判定樹原理進行簡要論述。
  7. Bayesian classification is based on bayesian theorem. it can be comparable in interpretability with decision tree and in speed with neural network classifiers. bayesian classifiers have also exhibited high accuracy and speed when applied to large databases

    該演算法基於貝葉斯定理，可解釋性方面可以與判定樹相比，準確度可和神經網路分類演算法相媲美，用於大型數據庫時該演算法已表現出高準確度與高速度。
  8. It gives many important helps to enhance the security of the control system. the first chapter of this paper gives a brief introduction to the development of fault diagnosis and the basic knowledge of it. the second chapter dicusses the fault diagnosis based on the expert system. the third chapter introduces the fault diagnosis based on the fault tree analysis. the forth chapter gives the fault diagnosis based on the fuzzy system. the fifth chapter analysis the fault diagnosis based on the artificial neural network in detail. the last chapter is the most important part of the paper, in this chapter i give a new fault diagnosis based on the fuzzy neural network, and applicated it in the kj15a control system, obtain the good result

    本文的第一章介紹了故障診斷的發展概況與一些故障診斷的基本知識；第二章討論了基於專家系統的故障診斷；第三章論述了基於故樟樹分析法的故障診斷；第四章給出了基於模糊系統的故障診斷；第五章詳細分析了基於人工神經網路的故障診斷；第六章是本文的核心部分，它在綜合了前面的模糊系統和神經網路故障診斷的基礎上，提出了一種基於神經模糊網路故障診斷，並將該技術應用到「 kj15a礦井機車運輸監控系統」故障檢測上，取得了良好的效果。
  9. The grassland desertification information in huolinhe opencast coalmine is extracted from remote sensing imageries by using decision tree and back - propagation ( bp ) neural network, and the desertification during the past 16 years in the research field is analyzed

    摘要以位於草原區的霍林河露天煤礦為例，基於決策樹和bp神經網路，在荒漠化信息提取的基礎上，對研究區16年來的荒漠化情況進行了分析。
  10. A model of spatial decision support system based on spatial data mining was established after making research on application and integration of spatial data mining, " 3s " technology and environment model, which include designing of data base, knowledge base, model base and their management system, inference engine and intelligent data mining engine ; 2. a model using artificial neural networks to forecast in coast environment in fujian was established, and a method applying multivariate decision tree to remote sensing classification was presented ; 3. a novel and shortcut method realizing artificial neural networks was presented, and then we put forward method realizing decision tree and realized it in prototype

    論文的主要內容概括起來有： （ 1 ）對空間數據挖掘技術、 「 3s 」技術、環境模型在空間決策支持系統中的應用與集成進行了研究，提出了一種基於空間數據挖掘的環境調控空間決策支持系統的模型，包括模型庫及其管理系統、知識庫及其管理系統、數據庫及其管理系統、推理機以及數據挖掘智能引擎等的設計； （ 2 ）建立了人工神經網路在福建省海岸帶環境預測中應用的模型，提出了復合決策樹演算法在遙感分類的應用方法； （ 3 ）提出一種新穎的、簡便快捷的人工神經網路的實現方法，以及決策樹的實現方法，並在原型系統中作了實現。
  11. Tree growth prediction based on neural network technology

    基於神經網路的樹木生長預測
  12. Hybrid machine learning ( hml ) is the latest applying in the field of intelligent information process. it combines the induced learning based - on decision - making tree with the blocking neural network. and it provides a useful intelligent knowledge - based data mining technique

    混合機器學習是在智能信息處理上的最新應用，它把以決策樹為基礎的歸納學習與模塊化的神經網路演算法結合起來，從而提供了一種在知識基礎上進行證實和確認的行之有效的智能化數據挖掘過程，其核心演算法是id3和ftart 。
  13. In this paper, hybrid fuzzy neural networks architecture is proposed. a series of fuzzy neural subnetworks are integrated in this hybrid fuzzy neural networks to perform the task of fault classification. making use of the fault tree of the system to construct the subnetworks and building the hybrid neural network that includes several subnetworks to solve the questions of single neural network

    為此本文提出了一種復合模糊神經網路結構，該神經網路結構集成了一系列模糊神經子網路，來完成故障分類任務，以系統故障樹為設置子網的基礎，建立了含有多個子網的復合模糊神經網路，可以解決單一神經網路面臨的問題。
  14. In the execution process of data mining, the author reduces the dimensions with the method of neural network learning and produce rule sets with the method of decision tree learning

    在實施數據挖掘過程中，根據神經網路和決策樹方法各自固有的優點，將神經網路運用於屬性的規約，而將決策樹用於產生規則模型。
  15. This thesis also provides an in - depth analysis of neural network and decision tree to find out their respective merits and drawbacks, and performs a research on the superiority of the combination of these two technologies

    文章對神經網路和決策樹技術進行了深入的分析，研究其各自的優缺點，並分析了將這兩種技術結合在一起的可能性及優勢。
  16. And then, the thesis brings forward a new modeling method - abnormal customers recognition system based on generalized computing and classification and regression trees. the system is composed of multi - rules neural network learning part and classification and regression trees processing part

    然後，通過深入研究多準則神經網路和決策樹的特點，論文提出了將多準則神經網路應用於決策樹的建模方法? ?基於多準則神經網路和分類回歸樹的異動客戶識別系統。
  17. On the basis of the concepts of divide - and - conquer and rough - fine, dia build a set of algorithm models including two rough classifiers ( i. e. bdt and cmac ) and three fine classifiers ( i. e. clchfpp, cpn - wed, nne - ifm ) which integrate some techniques such as clustering, decision tree, neural networks, fuzzy logic and information fusion etc. and take advantage of them, so dia has a high accuracy and speed theoretically

    Dia系統中演算法模型的設計基於「分而治之」和「由粗到精」的模式識別思想，具體構建了兩種粗分器（ bdt和cmac ）及三種精分器（ clc - fpp 、 cpn - wed和nne - ifm ） ，這些分類器的設計綜合了聚類法、決策樹、神經網路、模糊邏輯以及信息融合等技術的應用，並使其優勢互補，因此在理論上具有識別精度高及速度快的優點。
  18. Resin concentration control based on artificial neural network

    基於神經網路的樹脂濃度控制
  19. Neural network makes label elements of filter dom tree as input, extract results as output, training via bp learning arithmetic

    神經網路將樣本集中過濾后的dom樹的標簽元素作為網路的輸入，標注抽取結果作為理想輸出，通過反向傳播學習演算法對網路進行訓練。
  20. It is demonstrated by simulation data. as for classifier, it presents the artificial neural network. based on three methods of modulation recognition and decision tree classifier and neural network classifier, experimentations have been carried through

    在分類器設計方面，介紹了利用神經網路進行模式識別的原理，採用前述的三種特徵提取方法，分別結合判決樹分類器和神經網路分類器對信號進行分類，並且進行了試驗論證。
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