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
The Linux kernel is one of the most interesting yet Least understood open-source projects. It is also a basis for developing new kernel code. That is why Sams is excited to bring you the latest Linux kernel development information from a Novell insider in the second edition of Linux Kernel Development. This authoritative, practical guide will help you better understand the Linux kernel through updated coverage of all the major subsystems, new features associated with Linux 2.6 kernel and insider information on not-yet-released developments. You ll be able to take an in-depth look at Linux kernel from both a theoretical and an applied perspective as you cover a wide range of topics, including algorithms, system call interface, paging strategies and kernel synchronization. Get the top information right from the source in Linux Kernel Development.
標簽： interesting open-source understood projects
上傳時間： 2015-06-30
上傳用戶：zyt
控制行業中重要的Least square parameter solution，里面使用了一個例子，可以將輸入改變然后使用
標簽： parameter solution square Least
上傳時間： 2014-02-13
上傳用戶：tb_6877751
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
The Linux kernel is one of the most interesting yet Least understood open-source projects. It is also a basis for developing new kernel code. That is why Sams is excited to bring you the latest Linux kernel development information from a Novell insider in the second edition of Linux Kernel Development. This authoritative, practical guide will help you better understand the Linux kernel through updated coverage of all the major subsystems, new features associated with Linux 2.6 kernel and insider information on not-yet-released developments. You ll be able to take an in-depth look at Linux kernel from both a theoretical and an applied perspective as you cover a wide range of topics, including algorithms, system call interface, paging strategies and kernel synchronization. Get the top information right from the source in Linux Kernel Development.
標簽： interesting open-source understood projects
上傳時間： 2015-07-26
上傳用戶：mpquest
Least Square - ARMA 算法的MATLAB代碼, 是頻譜分析(通常是在高級DSP這門課中會用到的)的常用算法
上傳時間： 2013-12-21
上傳用戶：zhangjinzj
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
