earplms.m

时间序列工具箱

function [a] = earplms(Y, p, UC);
% [a] = earplms(Y, p, UC);
% earplms	Estimate Coefficients of AR-Process
%	Y(t)-a*Y(t-1) = E(t)
% using the Least Mean Square method
%
%Input: Y	Signal
%	p	model order
%	UC	step size parameter (default 0.01/var{Y})
%Output:
%	a	AR-Parameters

%	Version 2.30
%	last revision 21.03.1998
%	Copyright (c) 1997, 1998 by Alois Schloegl
%	e-mail: [e-mail]	

% This library is free software; you can redistribute it and/or
% modify it under the terms of the GNU Library General Public
% License as published by the Free Software Foundation; either
% Version 2 of the License, or (at your option) any later version.
%
% This library is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
% Library General Public License for more details.
%
% You should have received a copy of the GNU Library General Public
% License along with this library; if not, write to the
% Free Software Foundation, Inc., 59 Temple Place - Suite 330,
% Boston, MA  02111-1307, USA.

ly = size(Y);
if ~sum(ly <= 1)
    error('Must be a vector.')
end
ly = max(ly);
if diff(ly)>0
	Y = Y';    % Make sure it's a row vector
end;	

a=zeros(p,ly);

if nargin < 3
	UC=0.01/var(Y);
end;

for i=2:p,
    e = Y(i) - Y(i-1:-1:1) * a(1:i-1,i-1);
    a(1:i-1,i) = a(1:i-1,i-1) + (UC * e) * Y(i-1:-1:1)';
end;

for i=p+1:ly,
    e = Y(i) - Y(i-1:-1:i-p) * a(:,i-1);
    a(:,i) = a(:,i-1) + (UC * e) * Y(i-1:-1:i-p)'; 
end;