Least Mean Square Newton Algorithm
標簽： Algorithm Square Newton Least
上傳時間： 2014-01-13
上傳用戶：sardinescn
Beamforming thesis describing Study of a various Beamforming Techniques And Implementation of the Constrained Least Mean Squares (LMS) algorithm for Beamforming
標簽： Beamforming Implementation describing Techniques
上傳時間： 2013-12-25
上傳用戶：wuyuying
System identification with adaptive filter using full and partial-update Generalised-Sideband-Decomposition Least-Mean-Squares
標簽： Generalised-Sideband-Decomp identification partial-update adaptive
上傳時間： 2017-09-13
上傳用戶：xcy122677
By building a nonlinear function relationship between an d the error signal，this paper presents a no— vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．
標簽： relationship nonlinear building function
上傳時間： 2015-10-22
上傳用戶：hzy5825468
This directory contains utility for implementing generic Reqursive Least Squares (RLS) algorithm. The example shows how one can use the utility to estamate the parameters of a simple linear discrete time system.
標簽： implementing Reqursive directory algorithm
上傳時間： 2014-01-06
上傳用戶：gtf1207
The module LSQ is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar-rotation algorithm is used to update the QR- factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill- conditioned problems, such as fitting polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back-substitution routine BKSUB2 which it calls, the routines behave as in AS 274.
標簽： least-squares unconstrained Statisti Applied
上傳時間： 2015-05-14
上傳用戶：aig85
最小平方近似法 (least-squares approximation) 是用來求出一組離散 (discrete) 數據點的近似函數 (approximating function)，作實驗所得的數據亦常使用最小平方近似法來達成曲線密合 (curve fitting)。以下所介紹的最小平方近似法是使用多項式作為近似函數，除了多項式之外，指數、對數方程式亦可作為近似函數。關於最小平方近似法的計算原理，請參閱市面上的數值分析書籍
標簽： least-squares approximation approximating discrete
上傳時間： 2015-06-21
上傳用戶：SimonQQ
通過奇異值分解實現的最小二乘擬合算法 inear least-squares fit by singular value decomposition
標簽： decomposition least-squares singular inear
上傳時間： 2015-07-26
上傳用戶：bibirnovis
有監督自組織映射-偏最小二乘算法(A supervised self-organising map–partial least squares algorithm),可以用語多變量數據的回歸分析
標簽： self-organising supervised algorithm partial
上傳時間： 2015-10-22
上傳用戶：hfmm633
Partial Least Squares regression bilinear factor model 的輸出源碼
標簽： regression bilinear Partial Squares
上傳時間： 2013-12-03
上傳用戶：familiarsmile
