樣本方差矩陣 的英文怎麼說

中文拼音 [yàngběnfāngchājǔzhèn]
樣本方差矩陣 英文
sample variance matrix
  • 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...
  • 方 : Ⅰ名詞1 （方形; 方體） square 2 [數學] （乘方） involution; power 3 （方向） direction 4 （方面） ...
  • 差 : 差Ⅰ名詞1 （不相同; 不相合） difference; dissimilarity 2 （差錯） mistake 3 [數學] （差數） differ...
  • 矩 : 名詞1. （畫直角或正方形、矩形用的曲尺） carpenter's square; square2. （法度; 規則） rules; regulations 3. [物理學] moment
  • 陣 : Ⅰ名詞1 （作戰隊伍的行列或組合方式） battle array [formation]: 布陣 deploy the troops in battle fo...
  • 樣本 : sample book; specimen; advanced copy; sample; muster; scantling; instance; statistics
  • 方差 : dispersion
  • 矩陣 : [數學] matrix; array
  1. For the given sample points, and matrix formed by covariance function with sample points as parameters, when the number of sample points approaches infinite, it is proven that this matrix spectrum will approach the spectral approach theorem for positive - definite kernel of integral equation

    對給定的樣本點，由樣本點為變量的協方差函數構成的矩陣，當樣本點個數趨于無窮大時，證明此矩陣譜逼近於積分方程正定核的譜逼近定理。
  2. It is especially attractive for the downlinks and suppressing intercell mai. when multiuser detector is adapted in blind mode, it usually adopts eignvalue decomposition or singularvalue decomposition of received sample correlation matrix and tracking alrithgms, which result in high computational complexity. at the same time, approximation computation in tracking alrithgms also result in slow convergence

    為實現盲自適應檢測，通常採用對接收信號樣本矩陣進行特徵值分解（ evd ）或奇異值分解（ svd ）后進行跟蹤，由此帶來的子空間秩跟蹤使得實現復雜度很高；另一方面，在跟蹤演算法中考慮一些實際情況而作出近似處理，從而引起誤差積累和正交性誤差，導致每次跟蹤開始階段跟蹤速度變慢。
  3. A general stack equation of mobile robot based on analyzing the motion of planar motion object and the mathematical models of four different kinds of common wheels is developed, accordingly, the mobility of mobile robot is addressed and the forward and inverse solutions to speed for specific configuration driven by differential speed are derived. utilizing the muir and newman convention, the description of the posture transformation matrices between different coordinate frames and the solution for the speed of point located on these frames are introduced. according to posture estimation, a more accurate method, dead reckoning algorithm, is developed for a specified configuration characterized by differential speed motorization, and simulations of this algorithm and other traditional methods are carried out using matlab while traversing a circular path

    本文對兩輪差速驅動移動機器人的運動學及其本體緩沖設計進行了探討，在對平面運動物體運動分析的基礎上結合四種常用車輪的數學模型，推導出了一個通用的移動機器人堆積方程，在此基礎上分析了移動機器人的移動能力、並針對兩輪差速構型推導了速度正解與逆解；使用muir和newman的運動學建模方法，推導了移動機器人上點及連桿坐標系位姿、速度變換關系矩陣及求解方法；在移動機器人位姿識別方法中結合差速驅動構型對航位推演算法進行了分析：推導了一種理論精度較高的航位推算演算法，並使用matlab對其與傳統的推算演算法在跟蹤圓弧軌跡情況下進行了模擬；最後針對本文所研究的機器人給出了一種比較系統、可靠的緩沖結構設計思路，較好地解決了移動機器人作業過程中外界因素及本身設計中引入的各種不確定誤差問題；本論文研究成果已在本實驗室所開發的樣機上得到實現，經過應用與考核證明其中的分析與設計是切實可行的。
  4. Specifically, according to the w - w five parameters failure theory, the fracture criterion of crack is established, and the state of crack ( open or close ) is judged by the values of the crack strain. combining the two points, the predict - model about the failure of concrete material is established. this predict - model can predict 16 failure forms, and basing the different failure form predicted after crack, the stress - strain relationship matrix of concrete material is adjusted

    具體來講，根據w - w的五參數混凝土破壞理論，建立混凝土的開裂準則，根據開裂應變值來判斷裂縫是張開還是閉合，從而在兩者基礎上建立了裂縫的開裂預測模式，總共有16種開裂模式；裂后根據具體的開裂模式及殘余抗剪能力來調整混凝土的本構關系矩陣，即用等效剛度代替原有剛度，考慮到垂直於裂縫方向的剛度為零，這樣會使裂后的總剛出現病態，為此文中通過引用鬆弛系數來對出現裂縫的單元進行預處理，一方面可解決因過大的舍入誤差導致計算結果的不可信問題，另一方面就是可加速收斂。
  5. Under ideal conditions, adaptive array signal processing methods can get excellent performance and adaptive beamformers provide an improvement in array output signal - to - interference - plus - noise - ratio ( sinr ) in comparison with conventional beamforming. in practical operating circumstances, the performance of adaptive array signal processing methods degrade extremely due to existing errors

    但是，在實際系統中總存在有誤差，包括自適應訓練樣本有限次快拍引起的協方差矩陣的估計誤差和各種系統誤差，誤差使得實際陣列流形與理想陣列流形存在差異，這時自適應陣列信號處理的性能會急劇下降。
  6. In this thesis, we adopt the technique of statistical training, create a sample database of every kinds of expression face images, construct a matrix of the difference of each sample and average image, and reduce dimension by pca, then decrease the relativity of principle components by ica, and therefore get the character sub - space of face. when detecting a face, we adopt the method of disturbing principle components of model to match special facial image, which is called whole optimization method in this thesis

    論文採用統計訓練的思想，選擇包括各種表情變化的人臉圖像建立樣本庫，取所有樣本與平均圖像的差構造一個矩陣，利用主元分析方法進行降維，然後通過獨立元分析降低主元相關性，建立了人臉的特徵子空間；演算法採取對主元進行擾動優化匹配的方法檢測人臉，本文稱此方法為全局最優的方法。
  7. According to the requirements to pd pattern auto - recognition, this paper studies systematically the basic theories and realizable methods for auto - recognition of pd gray intensity image : ( 1 ) in the requirement of on - line pd monitoring for transformer, several discharge models are designed and the relevant experiment methods projected. with discharge model tests, a lot of discharge sample data is acquired. on the base of systematical research on recognition for pd gray intensity image, this paper puts forward two kinds of fractal features, the 2nd generalized dimensions of original pd images and fractal dimensions of high gray intensity pd images, and then the relevant extraction methods

    針對局部放電模式自動識別的需要，作者系統地研究了局部放電灰度圖像自動識別中的基本理論和實現方法： （ 1 ）根據變壓器局部放電在線監測的要求，設計了放電模型和實驗方法，並通過模型實驗獲得了大量放電樣本數據，為構造局部放電灰度圖像和採用bpnn進行識別作好準備； （ 2 ）研究了局部放電灰度圖像的構造方法以及降維構造32 32灰度和矩陣的方法；在用人工神經網路對局部放電進行模式識別時，分析了bp網路的優缺點，對典型bp網路的結構和學習訓練演算法提出了改進，採用帶有偏差單元的遞歸神經網路作為模式分類器；採用32 32灰度和矩陣進行bpnn識別結果表明這種方法是有效的。
  8. Based on the formers, this dissertation efficiently selects the face features abstracting using ica. with no decline of recognition rate, the feature dimension is reduced, so the course of recognition is accelerated. support vector machine pattern recognition method is based on vc dimension theory, adopting the srm principle and considering training error and the generalization ability, which has shown many special advantages in dealing with small samples, non - linear and pattern recognition in high dimension

    本文採用基於矩陣s的人臉表示方法，將ica特徵選擇的概念和演算法用於人臉特徵的提取和優化，在不影響識別率的情況下，降低了特徵維數，提高了識別速度；支持向量機（ svm ）模式識別方法基於vc維理論，採用結構風險化原理，兼顧訓練誤差和泛化能力，在解決小樣本、非線性及高維模式識別問題中表現出許多特有的優勢；對于多類問題，介紹並採用了「一對一」的策略進行svm分類器設計；對于圖像預處理，詳細介紹了幾何歸一化的演算法步驟。
  9. In this paper, an improvement is made through selecting a group of normal orthogonal vectors in feature subspace, to generate large amount of virtual training samples

    摘要在模式特徵子空間中選取一組標準正交向量，使用這組向量可以生成大量的虛擬訓練樣本，從而實現對協方差矩陣的優化。
  10. First we construct a covariance matrix from sample images, then compute the eigenvalues and corresponding eigenvectors of the covariance matrix, construct a feature matrix with the eigenvectors. then every images in database can be projected into the feature matrix and gain a projection vector, so does the input image. then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors

    我們首先根據採集的樣本圖像構造一個協方差矩陣，然後求取該矩陣的特徵值，以這些矩陣特徵值對應的特徵向量構造出一個特徵空間，然後將輸入圖像向該特徵空間映射，將獲取的映射系數與樣本庫中圖像的映射系數進行距離計算，根據計算出的距離判定輸入圖像與樣本圖像間的匹配程度。
  11. First we construct a covariance matrix from sample images, then compute the eigenvalues and corresponding eigenvectors of the covariance matrix, construct a feature matrix with the eigenvectors. then every image in database can be projected into the feature matrix and gain a projection vector, so does the input image. then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors

    然後，根據採集的樣本圖像構造一個協方差矩陣，求取該矩陣的特徵值，以這些矩陣特徵值對應的特徵向量構造一個特徵空間，將輸入圖像向該特徵空間映射，計算獲取的映射系數與樣本庫中各類圖像的映射系數的歐基里德距離，根據計算出的距離判定輸入圖像與樣本圖像間的匹配程度。
  12. The covariance of the training samples which are sampled from the antenna arrays is delivered into svm training machine after transformed suitably, then we use svm regression on the unknown samples according as the trained machine for getting the source location

    把陣列天線採集的訓練樣本的協方差矩陣進行適當變換之後送入svm訓練器進行學習，然後根據這個學習機對未知樣本進行svm的函數擬合，得到信源方向。
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