盲自適應(yīng)算法--遞推最小二乘恒模算法Recursive Least Squares Constant Modulus Algorithm for Blind Adaptive Array
標(biāo)簽： Recursive Algorithm Constant Adaptive
上傳時(shí)間： 2014-06-30
上傳用戶：helmos
contains documents about new insights into the recursive least squares algorithm and a sample matlab code for rls algorithm
標(biāo)簽： algorithm documents recursive contains
上傳時(shí)間： 2017-04-13
上傳用戶：我干你啊
System identification with adaptive filter using full and partial-update Recursive-Least-Squares
標(biāo)簽： Recursive-Least-Squares identification partial-update adaptive
上傳時(shí)間： 2013-12-30
上傳用戶：LouieWu
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(biāo)簽： implementing Reqursive directory algorithm
上傳時(shí)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(biāo)簽： least-squares unconstrained Statisti Applied
上傳時(shí)間： 2015-05-14
上傳用戶：aig85
最小平方近似法 (least-squares approximation) 是用來(lái)求出一組離散 (discrete) 數(shù)據(jù)點(diǎn)的近似函數(shù) (approximating function)，作實(shí)驗(yàn)所得的數(shù)據(jù)亦常使用最小平方近似法來(lái)達(dá)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(xiàng)式作為近似函數(shù)，除了多項(xiàng)式之外，指數(shù)、對(duì)數(shù)方程式亦可作為近似函數(shù)。關(guān)於最小平方近似法的計(jì)算原理，請(qǐng)參閱市面上的數(shù)值分析書(shū)籍
標(biāo)簽： least-squares approximation approximating discrete
上傳時(shí)間： 2015-06-21
上傳用戶：SimonQQ
通過(guò)奇異值分解實(shí)現(xiàn)的最小二乘擬合算法 inear least-squares fit by singular value decomposition
標(biāo)簽： decomposition least-squares singular inear
上傳時(shí)間： 2015-07-26
上傳用戶：bibirnovis
有監(jiān)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(yǔ)多變量數(shù)據(jù)的回歸分析
標(biāo)簽： self-organising supervised algorithm partial
上傳時(shí)間： 2015-10-22
上傳用戶：hfmm633
Partial Least Squares regression bilinear factor model 的輸出源碼
標(biāo)簽： regression bilinear Partial Squares
上傳時(shí)間： 2013-12-03
上傳用戶：familiarsmile
多項(xiàng)式曲線擬合 任意介數(shù) Purpose - Least-squares curve fit of arbitrary order working in C++ Builder 2007 as a template class, using vector<FloatType> parameters. Added a method to handle some EMathError exceptions. If do NOT want to use this just call PolyFit2 directly. usage: Call PolyFit by something like this. CPolyFit<double> PolyFitObj double correlation_coefficiant = PolyFitObj.PolyFit(X, Y, A) where X and Y are vectors of doubles which must have the same size and A is a vector of doubles which must be the same size as the number of coefficients required. returns: The correlation coefficient or -1 on failure. produces: A vector (A) which holds the coefficients.
標(biāo)簽： Least-squares arbitrary Purpose Builder
上傳時(shí)間： 2013-12-18
上傳用戶：宋桃子
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