LRU(最近最少使用算法) and MRU(最近最常使用算法)所謂的LRU(least recently used)算法的基本概念是:當(dāng)內(nèi)存的剩余的可用空間不夠時,緩沖區(qū)盡可能的先保留使用者最常使用的數(shù)據(jù),換句話說就是優(yōu)先清除”較不常使用的數(shù)據(jù)”,并釋放其空間
標(biāo)簽： LRU recently least used
上傳時間： 2014-01-03
上傳用戶：彭玖華
This program simulates plant identification least mean square (NLMS) alogrithm reference: 《LMS算法的頻域快速實現(xiàn)》
標(biāo)簽： identification alogrithm simulates reference
上傳時間： 2013-12-17
上傳用戶：kristycreasy
This program simulates plant identification using frequency block least mean square (FBLMS) alogrithm reference: 《LMS算法的頻域快速實現(xiàn)》 LMS is modified by XXX in XXX place, see details in XXX relevant document
標(biāo)簽： identification frequency simulates alogrith
上傳時間： 2016-02-29
上傳用戶：kytqcool
Please read them carefully before you write the package to their specific functions (at least 20 characters). As far as possible not to let the station for the time you have spent on that amendment. Have to extract the password to the password
標(biāo)簽： carefully functions specific package
上傳時間： 2016-03-30
上傳用戶：小儒尼尼奧
多項式曲線擬合 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
上傳時間： 2013-12-18
上傳用戶：宋桃子
計算最小二乘法ordinary least squares
標(biāo)簽： ordinary squares least 計算
上傳時間： 2013-12-15
上傳用戶：dongqiangqiang
C/C++ implementation of the Levenberg-Marquardt non-linear least squares algorithm. levmar includes double and single precision LM versions, both with analytic and finite difference approximated jacobians
標(biāo)簽： Levenberg-Marquardt implementation non-linear algorithm
上傳時間： 2013-12-28
上傳用戶：gxf2016
基于最小二乘原理的曲線擬合程序-A program of curve fitting based on least squares algorithm.
標(biāo)簽： algorithm program fitting squares
上傳時間： 2016-07-07
上傳用戶：VRMMO
weighted least square line extraction with original sensor data
標(biāo)簽： extraction weighted original square
上傳時間： 2014-01-11
上傳用戶：qiaoyue
最小二乘法(least squares analysis)是一種 數(shù)學(xué) 優(yōu)化 技術(shù)，它通過 最小化 誤差 的平方和找到一組數(shù)據(jù)的最佳 函數(shù) 匹配。 最小二乘法是用最簡的方法求得一些絕對不可知的真值，而令誤差平方之和為最小。 最小二乘法通常用于 曲線擬合 (least squares fitting) 。這里有 擬合圓曲線 的公式推導(dǎo)過程 和 vc實現(xiàn)。
標(biāo)簽： analysis squares least 最小二乘法
上傳時間： 2016-09-06
上傳用戶：cuibaigao
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