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function [h,y] = lms(x,d,delta,N)

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

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looking for code of matlab which do

Implement the LMS algorithm to estimate the filter coefficients w0,. w3 is assumed to be very small (try =0.01). Plot the learning curves: 1. The error e(n). 2. (J vs iteration steps). Where J is defined to be J=e2(n). 3. (10log10(J) vs iteration steps).