[求助]求LMS算法的编程程序
各位兄弟姐妹有LMS算法的麻烦你们发一发谢谢。。。。。。。。。
[ 本帖最后由 风花雪月 于 2006-11-12 08:10 编辑 ]
回复：（hmilykk）[求助]求LMS算法的编程程序
matlab lms 算法实例function =lms(xn,dn,M,delt,varargin)
% LMS Algorithm ,返回滤波器加权系数矩阵和误差向量
%
% 调用格式
% =lms(xn,dn,M,delt,itr)
% en=滤波器输出和d(n)的误差序列,为列向量
% wn=滤波器的加权参量序列,为一矩阵,其每行代表一个加权参量,每列代表一次迭代;初始化值设为0
% xn=输入列向量信号
% dn=期望列向量信号
% M=滤波器阶数
% delt=步长
% itr=迭代次数
%
% =lms(xn,dn,M,delt)
% 迭代次数为默认值,即等于x(n)的点数
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 输入输出参数的检查
%--------------------------------------------------------------------------
vin=length(varargin); Item=vin+4;
error(nargchk(4,Item,nargin)); % 检查输入变量数目是否合适,其中前四个参数必须输入
if nargout>2 % 检查输出变量数目是否合适
error('Too many output arguments');
end
%------------------------------------------------------------------------
Nx=length(xn); % x(n)的长度
if Nx~=length(dn) % 检查x(n)和d(n)长度是否相等
error('The length of x(n) is not equal to that of d(n)');
end
%------------------------------------------------------------------------
sizex=size(xn); % 检查输入向量是否为列向量
if sizex(1)xn=xn.';
end
sizedn=size(dn); % 检查期望信号向量是否为列向量
if sizedn(1)dn=dn.';
end
%-------------------------------------------------------------------------
itr=Nx; % 迭代次数取默认值
%-------------------------------------------------------------------------
% 当输入变量为5个时
if Item==5 % 确定迭代次数
itr=varargin{1};
if itr>Nx | itrerror('Too many or too few iterations');
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 参数的初始化
en=zeros(itr,1);
wn=zeros(M,itr); % 每行代表一个加权参量,每列代表一次迭代
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 迭代计算
for k=M:itr % 第k次迭代
x_tap=xn(k:-1:k-M+1);
en(k)=dn(k)-wn(:,k-1)'*x_tap;
wn(:,k)=wn(:,k-1)+2*delt*en(k,1)*x_tap;
end
[ 本帖最后由 风花雪月 于 2006-11-12 08:10 编辑 ]
回复：（hmilykk）[求助]求LMS算法的编程程序
给另一个，其实原理一样的。多看个程序，参考一下。关键是这里有一个规一化LMS,可以跟一般的LMS比较。
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%LMS; Normalized LMS
% filter parameters
%This program cannot run,because in defect of the "awgn" function,which is
%in communication bockset.so,this funciton is omitted temporarily to test other codes.
%
%
%as the results, the study time of the NLMS is shorter than LMS.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all;
%close all;
snr=20;
order=8;
Hn =[0.8783 -0.5806 0.6537 -0.3223 0.6577 -0.0582
0.2895 -0.2710 0.1278 -0.1508 0.0238 -0.1814
0.2519 -0.0396 0.0423 -0.0152 0.1664 -0.0245
0.1463 -0.0770 0.1304 -0.0148 0.0054 -0.0381
0.0374 -0.0329 0.0313 -0.0253 0.0552 -0.0369
0.0479 -0.0073 0.0305 -0.0138 0.0152 -0.0012
0.0154 -0.0092 0.0177 -0.0161 0.0070 -0.0042
0.0051 -0.0131 0.0059 -0.0041 0.0077 -0.0034
0.0074 -0.0014 0.0025 -0.0056 0.0028 -0.0005
0.0033 -0.0000 0.0022 -0.0032 0.0012 -0.0020
0.0017 -0.0022 0.0004 -0.0011 0 0 ];
Hn=Hn(1:order);
N=1000;
% r=wavread('C:\Matlab\work\Write_In_Paper\Shi_Yan.wav');
% r=r(1:N);
% r=awgn(r,snr,'measured');
EE=zeros(N,1); Loop=10; mu=0.3;
for nn=1:Loop
r=sign(rand(N,1)-0.5); % r=awgn(r,snr,'measured');
output=conv(r,Hn);
%output=awgn(output,snr,'measured');
win=zeros(1,order);
error=zeros(1,N)';
for i=order:N
input=r(i:-1:i-order+1);
e=output(i)-win*input;
win=win+mu*e*input'/(1+input'*input);
error(i)=error(i)+e^2;
end;
EE=EE+error;
end;
error=EE/Loop;
figure;
%error=10*log10(error(order:N));
plot(error,'r'); axis tight; grid on;
%figure;
%semilogy(error(order:N),'b'); grid; axis tight;
%subplot(212);
%plot(win,'r*');
%hold on; plot(Hn,'b'); grid; axis tight;
snr=20;
order=8;
Hn =[0.8783 -0.5806 0.6537 -0.3223 0.6577 -0.0582
0.2895 -0.2710 0.1278 -0.1508 0.0238 -0.1814
0.2519 -0.0396 0.0423 -0.0152 0.1664 -0.0245
0.1463 -0.0770 0.1304 -0.0148 0.0054 -0.0381
0.0374 -0.0329 0.0313 -0.0253 0.0552 -0.0369
0.0479 -0.0073 0.0305 -0.0138 0.0152 -0.0012
0.0154 -0.0092 0.0177 -0.0161 0.0070 -0.0042
0.0051 -0.0131 0.0059 -0.0041 0.0077 -0.0034
0.0074 -0.0014 0.0025 -0.0056 0.0028 -0.0005
0.0033 -0.0000 0.0022 -0.0032 0.0012 -0.0020
0.0017 -0.0022 0.0004 -0.0011 0 0 ];
Hn=Hn(1:order);
N=1000;
% r=wavread('C:\Matlab\work\Write_In_Paper\Shi_Yan.wav');
% r=r(1:N);
% r=awgn(r,snr,'measured');
EE=zeros(N,1); Loop=150; mu=0.01;
for nn=1:Loop
r=sign(rand(N,1)-0.5); % r=awgn(r,snr,'measured');
output=conv(r,Hn);
%output=awgn(output,snr,'measured');
win=zeros(1,order);
error=zeros(1,N)';
for i=order:N
input=r(i:-1:i-order+1);
e=output(i)-win*input;
win=win+mu*e*input';
error(i)=error(i)+e^2;
end;
EE=EE+error;
end;
error=EE/Loop;
hold on;
%error=10*log10(error(order:N));
plot(error); axis tight; grid on;
% semilogy(error(order:N),'r'); grid; axis tight;
% subplot(212);
% plot(win,'r');
% hold on; plot(Hn,'b'); grid; axis tight;
[ 本帖最后由 风花雪月 于 2006-11-12 08:11 编辑 ] 要的就是这个！谢谢楼上的诸位！ 在“数字信号处理及其Matlab实现”（FTP中有该书）有LMS计算的函数：
function = lms(x,d,delta,N)
% LMS Algorithm for Coefficient Adjustment
% ----------------------------------------
% = lms(x,d,delta,N)
% h = estimated FIR filter
% y = output array y(n)
% x = input array x(n)
% d = desired array d(n), length must be same as x
% delta = step size
% N = length of the FIR filter
%
M = length(x); y = zeros(1,M);
h = zeros(1,N);
for n = N:M
x1 = x(n:-1:n-N+1);
y = h * x1';
e = d(n) - y;
h = h + delta*e*x1;
end
供楼主参考。
[ 本帖最后由 风花雪月 于 2006-11-12 08:11 编辑 ]
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