代码搜索：NLMS

找到约 97 项符合「NLMS」的源代码

代码结果 97
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www.eeworm.com/read/493843/6391476

m nlms.m

function [A,E] = nlms(x,d,beta,nord,a0) %NLMS Normalized LMS adaptive filtering algorithm. %--- %USAGE [A,E] = nlms(x,d,beta,nord,a0) % % x : input data to the adaptive filter. %
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www.eeworm.com/read/492149/6424356

m nlms.m

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www.eeworm.com/read/263526/11357748

m nlms.m

clear all close all hold off%系统信道权数 sysorder = 5 ;%抽头数 N=1000;%总采样次数 inp = randn(N,1);%产生高斯随机系列 n = randn(N,1); [b,a] = butter(2,0.25); Gz = tf(b,a,-1);%逆变换函数 h= [0.0976;0.2873;0.3360;0.2210;
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www.eeworm.com/read/366784/9798818

m nlms.m

kk=1000;sample_n=32000; n1=dataread0('D:\work\DAT23NNR.DAT',sample_n,kk); n2=dataread0('D:\work\DAT23NNP.DAT',sample_n,kk); noise=wavread('D:\work\dat\noise.wav'); x=n1/max(n1);y=n2/max(n1);% no
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www.eeworm.com/read/411038/11258739

m nlms.m

function [e,W]=nlms(mu,M,u,d,a); % Normalized LMS % Call: % [e,w]=nlms(mu,M,u,d,a); % % Input arguments: % mu = step size, dim 1x1 % M = filter length, dim 1x1 % u = input signal, dim Nx1 % a
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www.eeworm.com/read/148463/12465267

m nlms.m

function [W,e,Xout] = NLMS(x,d,N,mu,alpha,Xin,Winit); % [W,e,Xout] = NLMS(x,d,N,mu,alpha,Xin,Winit); % % Implementation of the normalized least mean square algorithm. % For complex inputs, this al
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www.eeworm.com/read/300120/13933821

m nlms.m

% This is the Normalized Least-Mean-Square Algotithm(NLMS) of two_channel acoustic echo cancellation. clear all clc tic %-load the impulse response-------------% load g_1 Imp
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www.eeworm.com/read/300120/13933833

m nlms.m

% This is the Normalized Least-Mean-Square Algotithm(NLMS) of two_channel acoustic echo cancellation. clear all clc tic %-load the impulse response-------------% load g_1 Imp
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www.eeworm.com/read/192078/8408273

m nlms1.m

%NLMS1 Problem 3.3 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of signal to predict % sigman - standard
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www.eeworm.com/read/192078/8408296

m nlms2.m

%NLMS2 Problem 4.4 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of reference signal % sigman - standard de
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