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smoothing parameter中文是什么意思

  • 勻滑參數(shù)

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  • And a criteria to choose the smoothing parameter of the bandwidth is suggested
    另外,我們給出了的光滑參數(shù)的選取方法
  • The choice of smoothing parameter can be automatically obtained by using generalized cross-validation function
    自編程序進行計算,得到了回歸參數(shù)向量和樣條函數(shù)的補償最小二乘估計。
  • (2 ) in this paper, using [ v, g ( ti ) ] n, h = m ( v, ( 1 ) yi ( 1 ) …, vi ( h ) yi ( h ) ) to the estimator of and g ( t ) of the semi-parametric regression model, and h is the smoothing parameter
    (2)對半?yún)?shù)回歸模型中的參數(shù)部分和非參數(shù)部分g(t),用最近鄰中位數(shù)估計去估計它們,其中h稱為光滑參數(shù)。
  • The one-step non-interior continuation method of chen and xiu for lcps is refined in chapter four . it is extended to nlps . the control parameter is replaced by the smoothing parameter in the perturbation term of newton step
    第四章細化了chen-xiu求解線性互補問題的一步非內點延拓算法,并且推廣到非線性互補問題。
  • Based on tikhonov regularization method and considering sufficiently the practice of geodesy, the main thread of the choice of the regularizer and the determination of the smoothing parameters is grasped and the in-depth research on the ill-posed problems in geodesy is carried out . a framework of the theory and methods of systematically processing the ill-posed problems has been established and tikhonov regularization method has been evolved in this paper
    本文基于tikhonov正則化方法和歐吉坤研究員提出的選權擬合的研究思路,充分考慮大地測量實際,抓住正則化矩陣的選取和正則化參數(shù)的確定這條主線,對大地測量中的不適定問題進行了深入研究,建立起了一套較系統(tǒng)的不適定問題處理理論及方法的框架,進一步發(fā)展了tikhonov正則化方法。
  • The main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed; the xor problem is implemented by using pnn . a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons . simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn
    本文主要分析了pnn隱層神經(jīng)元個數(shù),隱中心矢量,平滑參數(shù)等要素對網(wǎng)絡分類效果的影響,并用pnn實現(xiàn)了異或邏輯問題;提出了一種新的pnn有監(jiān)督學習算法:用學習矢量量化對各類訓練樣本進行聚類,對平滑參數(shù)和距離各類模式中心最近的聚類點構造區(qū)域,并采用遺傳算法在構造的區(qū)域內訓練網(wǎng)絡,實驗表明:該算法在分類效果上優(yōu)于其它pnn學習算法
  • The main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed; the xor problem is implemented by using pnn . a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons . simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn
    本文主要分析了pnn隱層神經(jīng)元個數(shù),隱中心矢量,平滑參數(shù)等要素對網(wǎng)絡分類效果的影響,并用pnn實現(xiàn)了異或邏輯問題;提出了一種新的pnn有監(jiān)督學習算法:用學習矢量量化對各類訓練樣本進行聚類,對平滑參數(shù)和距離各類模式中心最近的聚類點構造區(qū)域,并采用遺傳算法在構造的區(qū)域內訓練網(wǎng)絡,實驗表明:該算法在分類效果上優(yōu)于其它pnn學習算法
  • For yield map generating, a local-averaging interpolation method with a random error yield data filter was designed; and for field information distribution map generating, an idw interpolation method with a smoothing parameter was used . 7 . fuzzy integrative evaluation and cluster analysis was used for field management zoning
    在田間信息分布圖生成與插值方法的研究方面,設計了用于生成產量分布圖具有數(shù)據(jù)過濾功能的局部平均法;并選用了一種帶有平滑參數(shù)idw插值方法;該方法可以有效的防止插值結果中出現(xiàn)數(shù)值突然變化的奇異點。
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