📄 tlms2.m
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%TLMS2 Problem 1.1.1.2.2
%
% '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
% alpha - factor used in transformed-input power estimation
% gamma - small constant to avoid division by zero
% 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
salpha=1-alpha; % auxiliary factor
for j=1:(N+1),
T(1,j)=1/sqrt(N+1);
for i=2:(N+1),
T(i,j)=sqrt(2/(N+1))*cos(pi*(i-1)*(2*j-1)/2/(N+1));
end
end % DCT-Transform matrix
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
TW=zeros(L,1); % initial transformed coefficient vector
sigma2=zeros(L,1); % initial transformed-input power
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
S=T*X; % transformed input vector
d=Wo'*X; % desired signal sample
y=TW'*S; % output sample
sigma2=salpha*sigma2+alpha*S.*S;
% transformed-input estimated power
isigma2=1./(sigma2+gamma); % inverse
e=d+n(k)-y; % errror sample
TW=TW+2*mu*isigma2.*(e*S);
% new transformed 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
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