Description: 若不希望用与估计输入信号矢量有关的相关矩阵来加快LMS算法的收敛速度，那么可用变步长方法来缩短其自适应收敛过程，其中一个主要的方法是归一化LMS算法（NLMS算法），变步长 的更新公式可写成
W（n+1）=w（n）+ e（n）x（n）
=w（n）+ （3.1）
式中， = e（n）x（n）表示滤波权矢量迭代更新的调整量。为了达到快速收敛的目的，必须合适的选择变步长 的值，一个可能策略是尽可能多地减少瞬时平方误差，即用瞬时平方误差作为均方误差的MSE简单估计，这也是LMS算法的基本思想。
-Want to estimate if the input signal vector and the relevant matrix to speed up the convergence rate of LMS algorithm, then the variable step size method can be used to shorten its adaptive convergence process, one of the main method is normalized LMS algorithm (NLMS algorithm) , variable step-size update formula can be written W (n+ 1) = w (n)+ e (n) x (n) = w (n)+ (3.1) where, = e (n) x (n) the right to express filter update vector iterative adjust the volume. In order to achieve the purpose of fast convergence, we must choose the appropriate value of variable step size, a possible strategy is as much as possible to reduce the instantaneous squared error, which uses the instantaneous squared error as the mean square error MSE of the simple estimate, which is the basic LMS algorithm思想. Platform: |
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Author:闫丰 |
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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: |
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Author:闫丰 |
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Description: MATLAB code on linear minimum mean square error (LMMSE) estimation and its application to the problem of channel equalization in digital communication systems.
amr amin: code on the application of channel equalization in digital communication systems. - MATLAB code on linear minimum mean square error (LMMSE) estimation and its application to the problem of channel equalization in digital communication systems.
amr amin: code on the application of channel equalization in digital communication systems. Platform: |
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Author:amr |
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Description: 均方误差信噪比 峰值信噪平均绝对误差的计算-Write MATLAB functions that take two grayscale images as input, and calculate the
following image difference metrics:
Mean Squared Error (MSE)
Signal to Noise Ratio(SNR)
Peak Signal to Noise Ratio (PSNR)
Mean Absolute Error (MAE)
Process the given test images “Lena” with its original and after-adding-noise Platform: |
Size: 3072 |
Author:宁可 |
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Description: matlab下ESN的工具This software is intended for research use by experienced Matlab users
and includes no warranties or services.-In alphabetical order here is what the routines are doing:
analogToUnitCoded:
- helper function for coding a 1-dim analog signal into a unit-coded
signal (aka "local coding")
compute_NRMSE:
- computes the normalized mean squared error of the esn, given the
output of the esn and the actual teacher.
compute_statematrix:
- runs the input through the ESN. The reservoir (plus input) states of
the units are collected in matrix which is returned by the function Platform: |
Size: 67584 |
Author:max |
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Search - mean squared error matlab