樣本特徵函數 的英文怎麼說

中文拼音 [yàngběntèzhǐhánshǔ]
樣本特徵函數 英文
sample characteristic function
  • 樣 : Ⅰ名詞1. （形狀） appearance; shape 2. （樣品） sample; model; pattern Ⅱ量詞（表示事物的種類） kind; type
  • 本 : i 名詞1 （草木的莖或根）stem or root of plants 2 （事物的根源）foundation; origin; basis 3 （本錢...
  • 特 : Ⅰ形容詞（特殊; 超出一般） particular; special; exceptional; unusual Ⅱ副詞1 （特別） especially; v...
  • 徵 : 名詞[音樂] （古代五音之一 相當于簡譜的「5」） a note of the ancient chinese five tone scale corre...
  • 函 : 名詞1. [書面語] （匣; 封套） case; envelope 2. （信件） letter 3. （姓氏） a surname
  • 數 : 數副詞（屢次） frequently; repeatedly
  • 樣本 : sample book; specimen; advanced copy; sample; muster; scantling; instance; statistics
  • 特徵 : characteristic; feature; properties; aspect; trait
  • 函數 : [數學] function函數計算機 function computer; 函數計算器 function calculator; 函數運算 functional operation
  1. In connection with the difference and distribution characteristic of the samples in sample space rs based on dga, a new self - adapted weight fuzzy omean clustering model of fault diagnosis of the power transformer based on the potential function is proposed. meanwhile, from the aspect of geometry characteristic of fc - divided in s dimension sample space, a method is proposed for the purpose of getting an effective adjacent radius, adaptive cluster number c and original cluster center of x sample set. for the diagnosis sample x, the property measure and diagnosis rule are proposed, which under the condition of potential density function that determine c number of optimal fuzzy cluster p1

    根據以變壓器dga數據為特徵量的樣本空間各樣本差異特性以及樣本在空間r ~ s的分佈特性，首次提出了基於勢函數自適應加權的變壓器絕緣故障診斷的模糊c -均值聚類模型；同時，從s維樣本空間的f ~ c -劃分幾何特性出發，提出了一種求取樣本集的類勢有效鄰域半徑和自適應求取聚類數和聚類中心初值的方法；對一個待診斷樣本，設計了基於類勢密度函數意義下的屬性測度和診斷準則。
  2. The data is nonlinearly mapped into high dimensional kernel space at first. then a set of feature vectors can be found such that the bhattacharyya distance of the classes mapped into lower dimensional feature space by feature vectors is maximized. thus the upper

    該演算法採用核函數把樣本非線性映射到高維核空間，在核空間中尋找一組最優的特徵向量，把樣本線性映射到低維特徵空間，使類別之間的bhattach刪a距離最大，從而使特徵空間中樣本的baycs分類誤差上界最小。
  3. In the phase of training, it gets the sampling data from the wave files which were stored in the voice library by using the mci functions. then calculates the character vector ( 12 ranks of lpc and lpcc ) and trains them by clustering method, so we get the templates used by speech - recognition, this templates were stored in the template library. in the state of recognition, after calculating the character vector of input voice, we compare it with the character vectors of templates, and then find the best one or refuse it

    系統的組成模塊與語音識別系統的基本構成模型基本一致，在訓練過程中，通過調用mci （ mcimultimediacontrolinterface ）提供的函數從語音庫中的波形文件中讀取采樣數據，分幀計算出由12維線性預測系數和12維線性預測倒譜系數構成的特徵矢量，並按照聚類的方法進行訓練，得到后續語音識別時需要的模板，存放于模板庫中。
  4. The basic principle of our algorithm is based on such observation that a majority of meaningful solitary wave solutions can be expressed as the polynomial forms in terms of " bell - shaped " function sech and " kink - shaped " function tanh which possessing localized property

    演算法的基本原理是基於這樣一種觀察，即非線性波方程絕大部分有物理意義的孤立波解都可以表示成具有局部性特徵的「鐘狀函數」 - sech和「扭狀函數」 - tanh的多項式。
  5. In this paper, we define quadratic spline as wavelet, do the two dimentional dyadic wavelet transform on cell image, and get local modulus maxima from wavelet transform ' s results - modulus and angles, so we can find the cell image ' s edge image in each scales, at last, we compute optimum scale of cell image edge detection, and receive a good edge image which synthesize the characters in each scale

    本文中我們用二次樣條小波作為小波基函數，對細胞圖像進行二維二進小波變換，計算小波變換結果的局部模極大值點，得到各個尺度下的細胞圖像的邊緣，計算細胞圖像邊緣檢測的最優尺度，最後得到綜合了各個尺度特徵的較好的細胞圖像的邊緣。
  6. Because reciprocating pump has complicated structure and more exciters, so its signal is a strong non - stationary signal, and carrying out fault feature extraction and diagnosis is very difficult to it, this text mainly researches on featute extraction of reciprocating pump ’ s valve vibration signal. this text introduces hht that huang put forward, it is a kind of signal processing method that suits for dealing with the stationary signal, and suits for non - stationary signal also. although the hilbert - huang transform ( hht ) is an effective tool processing the non - stationary signal, the hht based on emperical mode decomposition ( abbreviated as emd ) algorithm which adopts the cubic spline interpolation could n ' t acquire accurate characteristics for the strong non - stationary signals in that the spline produces an accurate result only under the condition that the data consists of values of a smooth function

    本文引入了huang等人提出的hht ， hht是一種既適合於處理平穩信號也是一種適合於處理非平穩信號的信號處理方法。盡管希爾伯特-黃變換（ hht ）是處理非平穩信號的有效工具，但基於經驗模態分解（簡稱emd ）的hht由於採用三次樣條插值而不能準確提取強非平穩信號的特徵，因為三次樣條插值只有在數據由光滑函數值構成的情況下才能產生精確的結果。為了解決這個問題，本文提出了基於改進的emd演算法，即採用分段三次hermite插值多項式（ pchip ）作為極值包絡的方法。
  7. In this paper, by studying the feature of the netflow data and the mib status of the network equipments, at the same time, in terms of analyzing the characteristics of network attack, worm spread, virus infection and network misuse behaviors, our work is based on the facts that most of the anomaly traffic in campus network has influences of the netflow data and network equipment status. an approach is present to assess the threats of the traffic in terms of five factors : the traffic bytes distribution, flow number distribution, packets number distribution, equipment cpu utilization and the memory utilization. the weight of each factor is computed and determined by fuzzy relation matrix 。 an prototype system is designed to test the method and the results are analyzed to evaluate the availability of our method

    本文研究了netflow流數據的特徵和網路設備運行狀態數據，分析了校園網網路異常攻擊、蠕蟲病毒和網路濫用行為的特點，基於大多數的網路流異常必然反映在網路網路流量數據特徵的變化以及網路設備運行狀態的改變這樣一個事實，提出了一套基於網路流量和網路設備運行狀態的異常威脅評估方法，確定了5種威脅評估因素:網路流帶寬分佈、網路流數量分佈、網路流包數量分佈、網路設備cpu利用率、網路設備內存利用率，並採用模糊關系矩陣方法計算和分配這5種評估因素在評估函數中的權重。
  8. Svm is a kind of universal learning algorithm, which developed from statistical learning theory, that is, small sample learning theory proposed by vapnik. it can represent complicated functions especially in high dimensions, which can avoid the trouble of the dimension tragedy that happened in general algorithm. it also will not affect the system performance by using original data of the array for the eigenvector directly

    支持向量機是由vapnik等人提出的小樣本統計理論? ?統計學習理論發展而來的一種新的通用學習演算法，特別在高維空間中表示復雜函數，避免了常規演算法「維數災難」等麻煩問題，可直接用陣列原始數據作為特徵向量而不影響系統性能。
  9. Their properties are discussed in details. furthermore, the relation between basic splines and bsc curves and surfaces is given. the third part is focused on bbc curves and surfaces, which includes the equation and properties of the curves and surfaces, and the relationship between be zier and bbc curves and surfaces

    討論bsc函數的構造、表示及其性質， bsc曲線曲面的表示方法、參數方程以及它們的本質特徵， bsc曲線曲面和b樣條曲線曲面的關系， bsc曲線曲面的相關演算法；第三部分是關于bbc曲線曲面的基本理論。
  10. By representing the essential features of section function, and presenting the theorem on discussing the limit, continuity and derivative at the boundary of section function, the article shows the reason why to choose the boundary of section function to discuss, and the method how to discuss them, while discussing the limit, continuity and derivative of section function

    摘要文章通過闡述分段函數的本質特徵，給出討論分段函數在分界點處極限、連續性及導數的定理，解決在討論分段函數的極限、連續性及導數時，為什麼要在分段函數的分界點處進行討論以及怎樣討論的問題。
  11. The class labels of the training samples are introduced during the training of the basis function to constrain the intra - class variations of the features. the features produced by the new sparse coding have large inter - class variations and small intra - class variations, thus the recognition performance of the reinforcement learning based sparse coding is better than that of traditional sparse coding

    在基函數的訓練過程中，通過引入訓練樣本的類別信息來限制特徵類內距離的增加，用這類方法獲得的特徵既有較大的類間距離，又有較小的類內距離，識別性能得到了較大的提高。
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