稀疏模式 的英文怎麼說

中文拼音 [xīshūmóshì]
稀疏模式 英文
arsitypattern
  • 稀 : Ⅰ形容詞1 （事物出現得少） rare; scarce; uncommon 2 （事物之間距離遠; 空隙大） sparse; scattered 3...
  • 疏 : Ⅰ動詞1 （疏通） dredge （a river etc ）2 （疏忽） neglect 3 （分散; 使從密變稀） disperse; scatte...
  • 模 : 模名詞1. （模子） mould; pattern; matrix 2. （姓氏） a surname
  • 式 : 名詞1 （樣式） type; style 2 （格式） pattern; form 3 （儀式; 典禮） ceremony; ritual 4 （自然科...
  • 稀疏 : few and scattered; few and far between; thin; sparse
  • 模式 : model; mode; pattern; type; schema
  1. ( 4 ) on the efficient method for the dynamical core of the new generation multi - scale forecasting model i ) we present a new multi - level sparse approximate inverse preconditnioner for the complicated 3 - d helmholtz equations in the new generation weather forecasting model. as a result, the new sparse approximate inverse preconditioned gcr and gmres algorithms are given and successfully applied in the dynamical core. numerical tests show that the new algorithms perform very efficiently, and can greatly improve the efficiency of numerical model

    對此，本文提出了一種基於逐層門限技術的近似逆矩陣稀疏模式預選方法，並構造了相應的稀疏近似逆預條件子，結合gcr演算法和g州[ r衛s演算法，首次將逐層門限稀疏近似逆預條件子應用於新一代多尺度預報模式動力內核的實際計算，數值實驗表明這里給出的方法可以大大提高數值模式的計算效率。
  2. Vegetation construction should n ' t be confined to such a land use pattern as " the 28 - word general plan " put forward by zhu xianmo. the objective reality of thin woods and sparse shrubberies in the forest - steppe zone cannot be ignored

    植被建造不應局限於一種土地利用模式，如朱顯謨「 28字方略」 ，不能無視疏林及稀疏灌叢在森林草原地帶的客觀存在。
  3. This paper applies generalized multipler method to translate convex quadratic programs with equal constraints and non - negative constraints into simple convex quadratic programs with non - negative constraints. the new algorithm is gotten by solving the simple quadratic program. it avoids the computation of inverse matrix and exploits sparsity structure in the matrix of the quadratic form. the results of numerical experiments show the effectiveness of the algorithm on large scale problems

    根據廣義乘子法的思想，將具有等式約束和非負約束的凸二次規劃問題轉化為只有非負約束的簡單凸二次規劃，通過解簡單凸二次規劃來得到解等式約束和非負約束的凸二次規劃新演算法，新演算法不用求逆矩陣，這樣可充分保持矩陣的稀疏性，用來解大規模稀疏問題.數值結果表明：在微機486 / 33上就能解較大規模的凸二次規劃
  4. The kanerva ' s sparse distributed memory ( sdm ) tackles the problem of training large data patterns and extendes the storage mode of existing computer. but it ' s address array produced randomly ca n ' t reveal the distribution of patterns and it has ' t the ability of function approximation for its learning rule

    Kanerva的稀疏分佈存儲（ sdm ）模型解決了大維數樣本的訓練問題，推廣了現有計算機的存儲方式。但其地址矩陣的隨機預置方式不能反映樣本的分佈，並且sdm的學習方式使之不能用於函數逼近及時間序列預測問題。
  5. The key issue of the study is to solve the effective exhaustion item type of the small - scale original numerator and sparse data set, so it can make analysis on all the possibilities of the containing type

    研究的核心問題是解決對小規模原子項和稀疏數據集進行有效的窮舉項類型，從而進行所有蘊涵式可能性的分析。
  6. Sad is able to differentiate models in form of subroutines, fully exploit the sparsity of the models and need few additional operations. it is concluded that sad is very suitable for the process models that have relatively simple computational structure and consist mainly of polynomials

    符號自動微分具有可對子程序形式的函數求導、可以充分利用模型的稀疏性、無需輔助操作的優點，非常適合針對結構相對簡單、計算式以多項式為主的過程系統模型求導。
  7. The relative frequency training ( rft ) method is used to estimate the model parameters. and the problem of the data sparseness is solved through the backing off data smoothing algorithm

    同時採用了回退式參數平滑演算法來解決了一階隱馬爾可夫模型的數據稀疏問題。
  8. In nature, opf belongs to a kind of large scale nonlinear programming problem with equality and inequality constraints, having a non - convex, high - dimension and highly sparse characteristic

    Opf本質上屬於一個具有等式和不等式約束的大規模非線性規劃問題，具有非凸、高維數、高度稀疏的特性。
  9. Unbound mode is also suited for spreadsheet - like or sparsely populated tables

    取消綁定模式還適用於類似於電子表格的表或稀疏填充的表。
  10. In this thesis, we propose an efficient nmfs + rbf aggregate framework for fr, in which non - negative matrix factorization with sparseness constraints ( nmfs ) is firstly applied to learn either the holistic representations or the parts - based ones by constraining the sparseness of the basis images, and then the rbf classifier is adopted for pattern classification

    本文提出了一種基於非負矩陣稀疏分解（ non - negativematrixfactorizationwithsparsenessconstraints ， nmfs ）和rbf神經網路的人臉識別方法。通過控制稀疏度， nmfs演算法既可提取人臉全局也能提取局部特徵，再運用rbf神經網路進行模式分類。
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