cluster method 中文意思是什麼

cluster method 解釋
星團方法
  • cluster : n 1 叢集；叢；（葡萄等的）串，掛；（花）團；（秧）蔸；組。2 （蜂、人等的）叢，群，群集。3 【物理...
  • method : n 1 方法，方式；順序。2 （思想、言談上的）條理，規律，秩序。3 【生物學】分類法。4 〈M 〉【戲劇】...
  1. In accordance with the problem that the fcm algorithm is quite time - consuming for search out cluster cancroids and may not be suitable for on - line modeling and control. this dissertation proposed an improved fuzzy identification method based multistage random sampling fuzzy c - means clustering algorithm ( mrfcm ). it has higher approximate precision and the cpu time has slowed down sharply compared with the common fuzzy

    Johnyen和liangwang介紹了幾種應用於模糊模型的信息優化準則，本論文在此基礎上對統計信息準則進行一些改進，並與快速模糊聚類和正交最小二乘方法結合，提高了模型的辨識精度和泛化能力。
  2. First, based on the historical data of 20 years of henan province, the cloud seeding operation cases in april and october in the central region of henan province were evaluated by cluster - analysis - based floating control historical regression method with uniform precipitation and atmospheric precipitable water as meteorological covariates, cluster - analysis - based floating control historical regression method with uniform precipitation as meteorological covariate, and floating control historical regression method ( fcm )

    首先，根據河南省近20年的歷史資料，分別用以降水量為協變量的ca - fcm方法、以降水量和整層大氣可降水量為協變量的ca - fcm方法和浮動對比區歷史回歸統計檢驗方法（ fcm ） ，對河南省4月和10月增雨作業進行評估。
  3. Then, six evaluation methods ( double ratio analysis evaluation method using regional rainfall tendency control for single cloud seeding operation case, regression analysis evaluation effects on the bases of regional correlation and developing tendency of rainfall, multiple regression analysis evaluation effects on the bases of regional developing tendency of rainfall, cluster - analysis - based floating control historical regression method with uniform precipitation and atmospheric precipitable water as meteorological covariates, cluster - analysis - based floating control historical regression method with uniform precipitation used as meteorological covariate, and float ing control historical regression method ) were compared and analyzed with the case of the cloud seeding operation on 5 april 2002 in henan province

    然後，以河南省2002年4月5日飛機增雨作業為個例，對作業區域趨勢對比雙比分析評估方案、區域趨勢相關回歸分析方案、區域趨勢多元回歸分析方案、以降水量為協變量的ca - fcm方法、以降水量和整層大氣可降水量為協變量的ca - fcm方法和fcm方法6種評價方案進行分析比較。
  4. In the end, cluster - analysis - based floating control historical regression method with uniform precipitation and atmospheric precipitable water as metoorological covariates could more validly evaluate efficiency of cloud seeding operations and significant level of ca - fcm method was higher than the other methods, because it adopted cluster analysis which highly improved the correlativity between rainfall distributions in the control area and target area, and used grid interpolation which enhanced exactness of calculating precipitation rainfall, and chose atmospheric precipitable water as the covariant which increased the inferential accuracy of natural rainfall on the cloud seeding operational area

    最後，得到以降水量和整層大氣可降水量為協變量的ca - fcm方法，由於採用聚類分析（ ca ）方法，提高了對比區和影響區相關性；採用網格插值技術提高了雨量的計算準確度；引入了不受催化影響的物理協變量（整層大氣可降水量） ，提高了作業區自然降水量估計值的準確性；所以評估效果最好，顯著水平高於0 . 05 。
  5. Previous researchers have always determined the sp atial distribution patterns ( sdp ) of castanopsis kawakamii with a sample - dis tance method. however, the distribution patterns may be affected by the quadrat si ze and, in the course of analysis, the density differences among the cluster plots are not considered ; therefore, differences of cluster plot size and the dispersi on degree among individuals of cluster plots can not be known. authers of this pa per have determined the spatial distribution patterns of castanopsis kawakamii population in different habitats by means of non - quadrat distance method and a nalysed the pattern intensity and grain of the sdp. the pattern intensity is defi ned with the relative density differences and the pattern grain can embody the d ispersion degree of the individuals in the plots, and the dispersion degree among the plots. the determined results are as follows. the intensities of the species range in order from strong to week : litsea mollifolia p. kawakamii i. purpure a r. cochinchinensis c. kawakamii c. carlessii d. oldphamii s. superba. the gra ins of the species queue in order from coarse to close : s. superba = litsea mollif olia r. cohinchinensis c. kawakamii = i. purpurea c. carlessii p. racemosam d. oldp hamii. these determined results tally basiclly with the results authers of this paper have got in determining the same plots by means of aggregate index access ing method. in view of this, it is held that the sdp of c. kawakamii is closely related to the habitats and biological features

    前人都是採用樣方方法對格氏栲種群數量的空間格局進行測定，而格局分佈有可能受樣方大小的影響，且分析過程中沒有涉及聚塊間密度差的問題，因而無法掌握種群的聚塊大小差別及聚塊內個體間的離散程度.本研究採用無樣方距離法，測定不同生境的格氏栲種群空間格局，分析格氏栲種群格局的強度和紋理.強度以聚塊和間隙的密度差來定義，紋理則是體現聚塊內個體間的離散程度與諸聚塊間的分離程度.測定結果表明，格氏栲種群格局強度從高到低排列次序為：木姜子蚊母樹冬青茜草樹格氏栲米櫧虎皮楠木荷；格局紋理從粗到細的順序是：木荷=木姜子茜草樹格氏栲=冬青米櫧蚊母樹虎皮楠.這一測定結果與作者採用聚集度指標測定相同樣地格氏栲種群空間格局的結果基本相符.因此，格氏栲空間格局類型及分佈與格氏栲生物學特性及生境的關系密切
  6. The three kinds of simulated point targets are designed and the rangeprofiles at aspect angle are computed. the radar target recognition method based on the optimal cluster centers is simulated and studied. it is discovered that the algorithm is effective when there are lots of training data, but noneffective when there are only a few training data

    2 、對最優聚類中心目標識別法進行模擬實驗並研究其識別性能，實驗結果表明該演算法在大樣本訓練數據時能得到較高識別率，是一種有效、可行的識別演算法，但在少樣本訓練數據時，所得識別率急劇下降。
  7. Evaluating reliability of existing building structures using grey cluster method

    建築結構現場檢測方法評析與展望
  8. This paper firstly applied sequential cluster method to set up the classification standard of precipitation state based on the fact that there are much uncertainty and imprecise characteristics in the precipitation course ; then this paper presented a method which is called markov chain with weights to predicted the future precipitation state by regarding the standardized self - coefficients as weights based on the special characteristics of precipitation being a dependent stochastic variable ; and applied this method to a real hydrological observation station with nearly 50 years precipitation information in shanxi province at last, an ideal result was obtained

    摘要首先基於降水過程存在大量不確定性、不精確性的特點，應用有序聚類的方法建立降水豐枯狀況的分級標準；然後針對降水量為相依隨機變量的特點，採取以規范化的各階自相關系數為權重，用加權的馬爾可夫鏈模型來預測未來降水的豐枯變化狀況；最後以山西省某水文站近50年的降水資料為實例對該方法進行了具體的應用，獲得了較為滿意的結果。
  9. Application of cluster method based on gravity theory

    萬有引力定律在聚類中的應用
  10. The paper attempt to applied sequential cluster method to set up the classification standard, then it regards the correlation coefficients of record values as weights and predicts the future loads by using markov chain model with weights. this method make the best of the information comprised in load series and solved the problem of obtaining weather information. not only the concrete value of the monthly sales electric energy but its range in the future is gained

    實際上，各月份的氣象、經濟因素之間具有一定的相關性，這些相關信息已經包含在負荷序列中，本文嘗試將馬爾可夫鏈理論應用於負荷預測之中，應用聚類分析的方法確定分級標準，將負荷分為不同的狀態，根據狀態之間的轉移概率來推測未來負荷的發展變化，並將觀測值之間的相關系數作為權值進行綜合預測，更加合理地利用了負荷序列中包含的信息，不僅可以預測出未來負荷的具體值，而且得到了其所屬的區間，具有一定的實用價值。
  11. Based on thorough analysing the factors which affect the seismic damage of single - story reinforced concrete industrial building, this paper introduces two kinds of methods which are applicable to prediction of seismic damage for single - story reinforced concrete industrial building. one is bp ( back propagation ) neural network method, which is based on the theory of artifical neural network. in this method, lm ( levenberg - marquardt ) optimization algorithm is applied to improve the performance of standard bp algorithm. the other is grey fixed weight cluster method, which is based on the theory of grey system

    本文在充分分析單層鋼筋混凝土柱工業廠房震害影響因素的基礎上，提出了兩種用於單層鋼筋混凝土柱工業廠房震害預測的新方法。一種是基於人工神經網路理論的bp神經網路方法。在bp神經網路方法中，採用改進的bp演算法（ bplm演算法）對網路進行訓練，有效地改善了標準bp演算法的不足之處。
  12. Simulation illustrates that the discovery result has dependency relation to the distribution of instance number of feature intervals, cluster method can help to generate more association rules with small quantity of frequent itemsets, and fuzzy discretization can obtain more frequent itemsets and association rules than simple boolean discretization method

    模擬表明，最終的挖掘結果與屬性各分段中實例的分佈特點有著密切聯系；聚類劃分在頻繁數據項集數目較少時可以獲得較多數量的關聯規則；利用模糊離散化可以獲得更多數量的頻繁數據項集和關聯規則。
  13. Following the two crows data mining process model, the product data, transaction data and customer ' s demographic data are accumulated, such data is preprocessed by the primary component analysis method which can low the connection of variants and reduce the number of variants. one model is built by the improved dynamic cluster method. the quality of the model ' s result will be improved with deleted the outlier data

    參照twocrows數據挖掘過程模型，首先收集客戶購買產品的類型、交易、屬性等數據；然後採用主成分分析法預處理這些數據，以降低數據之間的相關性和減少變量個數；接著採用改進的動態聚類方法建模，在聚類過程中剔除異常點，改善聚類的質量，最終得到一個客戶分片的模型，並對該模型作了比較詳盡的解釋。
  14. Based on web log of hannan univercity, this paper focused on the following aspects : 1 ) aanalyze and reserch technology of web log mining ; 2 ) bring forward a multi - path partitioning cluster method, which partions and clusteres according to user access path to improve effiency ; 3 ) bring forward a hierarchy search and iterative method to find frequent set

    本文結合海南大學web服務器日誌，主要研究了以下內容: 1 ） web服務器日誌挖掘技術進行了系統的分析和研究。 2 ）提出了多路徑分割聚類方法。這種方法根據用戶的訪問路徑進行分割聚類，有效地提高了聚類演算法的效率。
  15. Then the methods of ontology integration is studied, which falls into two main steps : using hierarchical cluster method to find similar concepts and using heuristic rule to merging similar discovered concepts

    本文接著對本體集成方法進行詳細研究。本體集成過程為先利用聚類演算法來找出相似概念，再利用啟發式規則進行相似概念合併處理。
  16. Research on text cluster method based on small world model and similarity principle

    和相似度的文本聚類演算法
  17. The number of fuzzy cluster neuron is equal to the fuzzy cluster number, and the neuron output is one element of the membership matrix. 4. use hybrid fuzzy neural network modeling method and recipe fuzzy cluster method, a robust method to recipe - changing was presented. a multi - continuous parameters and one - recipe to one - output fuzzy neural network was designed to model the fouling in batch process

    浙江大學博士學位論文4 、針對間歇過程中使用模糊神經網路建模和測量對于配方變化較為敏感的問題，使用配方模糊聚類方法和混合模糊神經網路建模方法，設計了一個由多個連續型操作參數輸入和1個離散型配方變量輸入的配方混合模糊神經網路，用於污垢的建模和預測。
  18. A membership degree matrix was presented in the end. 3. a hybrid fuzzy neural network modeling method was presented. on the basis of cct fuzzy neural network and fuzzy cluster method, this method can deal with discrete variables input

    3 、針對模糊神經網路不能接受離散標稱變量輸入的缺陷，在cct模糊神經網路和模糊聚類方法的基礎上，提出了一種混合模糊神經網路建模方法。
  19. From the angle of real estate development merchant, through a big amount of research and investigations, also based on consult lots of document means, the research mainly about the ways of cluster method, builds three mathematic models, such as blurring cluster analysis, dynamic cluster analysis, gray cluster analysis, the paper also has a further discussion about the practical application of models in making investment policy on real estate

    論文從開發商的角度出發，通過大量的調查研究，查閱大量文獻資料的基礎上，重點圍繞聚類方法展開研究，建立了模糊聚類分析、動態聚類分析、灰色聚類分析三個數學模型，探討了模型在房地產投資決策中的實際應用。
  20. Using the fuzzy cluster method, hybrid fuzzy neural network, association rules mining methods, etc. find and excavate the recipes, periodic fouling, and operation strategy rule in the batch process

    分別使用模糊聚類方法、混合模糊神經網路、關聯規則挖掘等知識發現方法對間歇過程中的配方、周期性污垢、操作策略規則等進行挖掘和處理。
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