lms3.m

来自「一个matlab实现IIR、FIR、LMS、NLMS等算法的m代码」· M 代码 · 共 47 行

%LMS3 Problem 1.1.1.2.1
%
%   'ifile.mat' - input file containing:
%      I - members of ensemble
%      K - iterations
%      a1 - coefficient of input AR process
%      sigmax - standard deviation of input
%      Wo - coefficient vector of plant
%      sigman - standard deviation of measurement noise
%      mu - convergence factor
% 
%   'ofile.mat' - output file containing:
%      ind - sample indexes 
%      M - misadjustment 

clear all	% clear memory
load ifile;	% read input variables
sigmav=sigmax*sqrt(1-a1^2);	
		% standard deviation of input to AR process
L=length(Wo);		% plant and filter length
N=L-1;			% plant and filter order
MSE=zeros(K,1);		% prepare to accumulate MSE*I
MSEmin=zeros(K,1);	% prepare to accumulate MSEmin*I

for i=1:I,		% ensemble
   X=zeros(L,1);    	% initial memory
   W=zeros(L,1);	% initial coefficient vector
   v=randn(K,1)*sigmav;		% input to AR process
   x=filter([1,0],[1,a1],v);	% input 
   n=randn(K,1)*sigman;		% measurement noise
   for k=1:K,		% iterations
      X=[x(k)		
         X(1:N)];	% new input vector
      d=Wo'*X;		% desired signal sample
      y=W'*X;		% output sample
      e=d+n(k)-y;	% error sample
      W=W+2*mu*e*X;	% new coefficient vector
      MSE(k)=MSE(k)+e^2;	% accumulate MSE*I
      MSEmin(k)=MSEmin(k)+(n(k))^2;	% accumulate MSEmin*I
   end
end

ind=0:(K-1);		% sample indexes
M=MSE./MSEmin-1;	% calculate misadjustment
save ofile ind M;	% write output variables