function [A,E] = lms(x,d,mu,nord,a0)%LMS	Adaptive filtering using the Widrow-Hoff LMS algorithm.%---%USAGE [A,E] = lms(x,d,mu,nord,a0)%%           x    : input data to the adaptive filter.%           d    : desired output%           mu   : adaptive filtering update (step-size) parameter%           nord : number of filter coefficients%           a0   : (optional) initial guess for FIR filter %		   coefficients - a row vector.  If a0 is omitted%		   then a0=0 is assumed.%%     The output matrix A contains filter coefficients.%        - The n'th row contains the filter coefficients at time n%        - The m'th column contains the m'th filter coeff vs. time.%        - The output vector E contains the error sequence versus time.%%  see also NLMS and RLS%%---------------------------------------------------------------% copyright 1996, by M.H. Hayes.  For use with the book % "Statistical Digital Signal Processing and Modeling"% (John Wiley & Sons, 1996).%---------------------------------------------------------------X=convm(x,nord);[M,N] = size(X);if nargin < 5,   a0 = zeros(1,N);   enda0   = a0(:).';E(1) = d(1) - a0*X(1,:).'; A(1,:) = a0 + mu*E(1)*conj(X(1,:));if M>1for k=2:M;    E(k) = d(k) - A(k-1,:)*X(k,:).';    A(k,:) = A(k-1,:) + mu*E(k)*conj(X(k,:));    end;end;