搜索资源列表
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1下载:
图像处理源代码,介绍了利用最小二乘法对BMP图像进行直线拟合的方法-image processing source code, introduced to the use of least squares to BMP images linear fitting method
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多项式最小二乘法
三弯矩插值法
lagrange多项式插值
多项式最小二乘法
龙贝格积分法
分段线性插值
三转角插值
这些是数值分析中常用的集中经典方法,运用matlab展示出来!-least squares polynomial interpolation three Moment Hangzhou polynomial interpolation polynomial least-squares method Long Bagby integration piec
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DotNet版的线性方程的解法,包括:高斯消元法,用于n阶非奇异矩阵;SVD分解法,求最小二乘解.目前还很难找到免费的DotNet版的数值计算程序.这里有源码(J#)和dll文件.-Kind version of the linear equation solution, including : Gaussian Elimination Act, for order n nonsingular matrix; SVD decomposition method, least squares sol
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DotNet版的线性方程的解法,包括:高斯消元法,用于n阶非奇异矩阵;SVD分解法,求最小二乘解.目前还很难找到免费的DotNet版的数值计算程序.这里有源码(J#)和dll文件.-Kind version of the linear equation solution, including : Gaussian Elimination Act, for order n nonsingular matrix; SVD decomposition method, least squares sol
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用C写的可以实现求解满秩线性方程组以及最小二乘曲线拟合的函数-written in C can be achieved full rank solving linear equations and least-squares curve fitting the function
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标准C写的数据直线拟合,最小二乘法处理,自己写的-write C standard linear data fitting, least squares method, wrote it myself
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最小二乘法的例程,可以对数据进行线性拟合.由于源码较短只能进行直线拟合,曲线拟合的源码我稍后上传.-least squares method of programming, with the linear data fitting. As the only source for a shorter fitted to a straight line Curve fitting, I later upload source.
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mimo-ofdm系统基于梳状导频的信道估计,使用最小二乘法,线性内插。-mimo - ofdm system based on comb-Pilot channel estimation, the use of least squares linear interpolation.
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GNU科学计算函数库GSL(GNU Scientific Library)是一个强大的C/C++数值计算函数库,它是一个自由软件,是GNU项目软件的一个部分,遵循GPL协议。函数库提供了大量的数值计算程序,如随机函数、特殊函数和拟合函数等等。整个函数库大约有1000多个函数,几乎涵盖了科学计算的各个方面。以下是整个函数库的目录:
Complex Numbers
Roots of Polynomials
Special Functions
Vectors and Matri
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PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis
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Numerical Computing with MATLAB (by Cleve Moler) is a textbook for an introductory course
in numerical methods, Matlab, and technical computing. The emphasis is on in-
formed use of mathematical software. We want you learn enough about the mathe-
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Toolbox for Numerical Computing with MATLAB (by Cleve Moler).
Numerical Computing with MATLAB (by Cleve Moler) is a textbook for an introductory course
in numerical methods, Matlab, and technical computing. The emphasis is on in-
formed u
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根据最小二乘法由一组点拟合一条直线-by the least squares method based on a group fitting a linear point
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The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data an
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灰色预测模型称为CM模型,G为grey的第一个字母,M为model的第一个字母。GM(1,1)表示一阶的,一个变量的微分方程型预测模型。GM(1,1)是一阶单序列的线性动态模型,主要用于时间序列预测。 一、GM(1,1)建模 设有数列 共有 个观察值 对 作累加生成,得到新的数列 ,其元素 (5-1) 有: 对数列 ,可建立预测模型的白化形式方程, (5-2) 式中: ——为待估计参数。分别称为发展灰数和内生控制灰数。设 为待估计参数向量 则 按最小二乘法求解, 有: (5-3) 式中: (5-
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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.
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数据预测算法,主要是一元n次方程的回归预测实现。* 预测分析--本算法只适用于有明显线性趋势的数据 * 默认为一元二次曲线方程法 * * 本程序主要涉及有两个算法 * 1.用最小二乘原理找到线性方程组的系数和常数。 * 2.解线性方程组 * 本程序在解线性方程组中,由于考虑到收敛性问题未采用迭代法,而是采用Gauss-Jordan消去法来解决。-data prediction algorithm is mainly one yuan n equation forecast to achieve
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pls算法工具箱。偏最小二乘回归≈多元线性回归分析+典型相关分析+主成分分析
,pls algorithm toolbox. Partial least-squares regression ≈ multiple linear regression analysis, canonical correlation analysis++ Principal component analysis
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递归最小二乘(RLS)是一种自适应滤波算法,它可以递归地找到最小化加权线性最小二乘代价函数与输入信号相关的系数。这种方法与其他算法相比较,例如最小均方(LMS),旨在减少均方误差。在RLS的推导中,输入信号被认为是确定性的,而对于LMS和类似的算法,它们被认为是随机的。(Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimiz
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%LSQCURVEFIT solves non-linear least squares problems.
% LSQCURVEFIT attempts to solve problems of the form:
% min sum {(FUN(X,XDATA)-YDATA).^2} where X, XDATA, YDATA and the values
% X returned by FUN ca
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