Description: 递归式最小均方（RLS）算法的基本思想是力图使在每个时刻对所有已输入信号而言重估的平方误差的加权和最小，这使得RLS算法对非平稳信号的适应性要好。与LMS算法相比，RLS算法采用时间平均，因此，所得出的最优滤波器依赖于用于计算平均值的样本数，而LMS（NLMS）算法是基于集平均而设计的，因此稳定环境下LMS（NLMS）算法在不同计算条件下的结果是一致的-Recursive least-mean-square (RLS) algorithm for the basic idea is to try to make in every moment of all the input signal in terms of re-evaluation of the weighted squared error and the smallest, which allows non-stationary RLS algorithm for adaptive signal better. Compared with the LMS algorithm, RLS algorithm uses the average time, therefore, the resulting optimal filter depends on the used to calculate the average number of samples, and the LMS (NLMS) algorithm is designed based on set average, and therefore a stable environment LMS (NLMS) algorithm in different conditions, the results of the calculation is consistent Platform: |
Size: 3072 |
Author:闫丰 |
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Description: 强局部加权回归算法由Cleveland[7]提出,主要利用局部观测数据对欲拟合点进行多项式加权拟合,并用最小二乘法进行估计.它综合了传统的局部多项式拟合,局部加权回归以及具有强鲁棒性的拟合过程
-Strong locally weighted regression algorithm by Cleveland [7] proposed, mainly using local observational data points on the polynomial fitting For weighted fitting, and estimated by least square method. It combines the traditional local polynomial fitting, locally weighted regression as well as strong robustness of the fitting process Platform: |
Size: 3072 |
Author:wanghuaqiu |
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Description: fit_rayleigh_pdf - Non Linear Least Squares fit of the Rayleigh distribution.
given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples.fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format:result = fit_rayleigh_pdf( x,y,W,hAx )
input: y - vector, samples of the histogram to be fitted
x - vector, position of the samples of the histogram (i.e. y = f(x,a))
W - matrix or scalar, a square weighting matrix of the size NxN where
N = length(y), or 0 to indicate no weighting is needed.
hAx - handle of an axis, on which the fitted distribution is plotted
output: result - structure with the fields
s - fitted parameter
VAR - variance of the estimation
type- weighted LS or not weighted LS
iter- number of iteration for the solution-fit_rayleigh_pdf - Non Linear Least Squares fit of the Rayleigh distribution.
given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples.fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format:result = fit_rayleigh_pdf( x,y,W,hAx )
input: y - vector, samples of the histogram to be fitted
x - vector, position of the samples of the histogram (i.e. y = f(x,a))
W - matrix or scalar, a square weighting matrix of the size NxN where
N = length(y), or 0 to indicate no weighting is needed.
hAx - handle of an axis, on which the fitted distribution is plotted
output: result - structure with the fields
s - fitted parameter
VAR - variance of the estimation
type- weighted LS or not weighted LS
iter- number of iteration for the solution Platform: |
Size: 2048 |
Author:resident e |
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Description: 一维杆的最小二乘无网格方法NATLAB程序，弹性问题-MESHLESS WEIGHTED LEAST-SQUARE PROGRAM TO SOLVE 1D ELASTIC PROBLEM- A 1D BAR OF UNIT LENGTH SUBJECTED TO BODY FORCE LINEARLY VARIATING WITH THE COORDINATE. THE LEFT END IS FIXED AND THE RIGHT IS FREE. Platform: |
Size: 2048 |
Author:xuxun |
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Description: 实现基于加权最小二乘法的电力系统状态估计算法，以于老师写的书里面四节点算例仿真-Implementation based on weighted least-square method of power system state estimation algorithm, with the teacher wrote the book four node example simulation Platform: |
Size: 2048 |
Author:张昭丞 |
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Description: 电力系统加权最小二乘法状态估计14节点或33节点都能运行-Weighted least square method for power system state estimation of 14 nodes or 33 nodes can run Platform: |
Size: 7168 |
Author:印 |
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Description: this is a Matlab code to create an indoor positioning system by using a weighted least square algorithm Platform: |
Size: 3072 |
Author:abbas970
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Description: 针对基于双曲线定位的DV-Hop算法中误差项的异方差性引起的定位误差大的问题，提出了一种基于加权双曲线定位的DV-Hop改进算法。算法分析了基于双曲线定位的DV-Hop算法模型中误差项的异方差性，用加权最小二乘法对异方差性进行纠正，对加权最小二乘法中的权值矩阵进行了推导并得到与跳数相关的最佳权值矩阵，使得误差项满足同方差性，所得估计值更加接近最佳线性无偏估计。仿真实验结果表明，所提算法在节点定位精度上较现有的基于双曲线定位的DV-Hop算法都有一定提高。(Aiming at the problem that the heteroscedasticity of observation errors leads to low locating accuracy in DV-Hop algorithm based on hyperbolic positioning, this paper proposes an improved DV-Hop algorithm which based on weighted hyperbolic positioning. The algorithm analysis the heteroscedasticity of the error term in the model and takes advantage of weighted least square method which corrects heteroscedasticity straightforwardly. Then, the weight matrix related to least hops between nodes is deduced, the estimators thus obtained are more close to the best linear unbiased estimator. The simulation results show that the location performance of the improved algorithm is better than that of the exsiting DV-Hop algorithm which based on hyperbolic positioning.) Platform: |
Size: 2048 |
Author:helios~
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Search - Weighted Least Square