📄 adim.m
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function [IR, CC, D] = adim(U, UC, IR, CC, arg5); % ADIM adaptive information matrix. Estimates the inverse
% correlation matrix in an adaptive way.
%
% [IR, CC] = adim(U, UC [, IR0 [, CC0]]);
% U input data
% UC update coefficient 0 < UC << 1
% IR0 initial information matrix
% CC0 initial correlation matrix% IR information matrix (inverse correlation matrix)
% CC correlation matrix%
% The algorithm uses the Matrix Inversion Lemma, also known as
% "Woodbury's identity", to obtain a recursive algorithm.
% IR*CC - UC*I should be approx. zero.
%
% Reference(s):
% [1] S. Haykin. Adaptive Filter Theory, Prentice Hall, Upper Saddle River, NJ, USA
% pp. 565-567, Equ. (13.16), 1996.
% $Id: adim.m 5090 2008-06-05 08:12:04Z schloegl $% Copyright (C) 1998-2003 by Alois Schloegl <a.schloegl@ieee.org>%% This program is free software: you can redistribute it and/or modify% it under the terms of the GNU General Public License as published by% the Free Software Foundation, either version 3 of the License, or% (at your option) any later version.%% This program 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 General Public License for more details.%% You should have received a copy of the GNU General Public License% along with this program. If not, see <http://www.gnu.org/licenses/>.
[ur,p] = size(U);
Mode_E = 1;
%if nargin < 4,
% m = zeros(size(U)+[0,Mode_E]);
%end;
if nargin<2,
fprintf(2,'Error ADIM: missing update coefficient\n');
return;
else
if ~((UC > 0) & (UC <1)),
fprintf(2,'Error ADIM: update coefficient not within range [0,1]\n');
return;
end;
if UC > 1/p,
fprintf(2,'Warning ADIM: update coefficient should be smaller than 1/number_of_dimensions\n');
end;
end;
if nargin<3,
IR = [];
end;
if nargin<4,
CC = [];
end;
if nargin<5,
arg5 = 6;
end;
if isempty(IR),
IR = eye(p+Mode_E);
end;
if isempty(CC),
CC = eye(p+Mode_E);
end;
D = zeros(ur,(p+Mode_E)^2);
%D = zeros(ur,1);
W = eye(p+Mode_E)*UC/(p+Mode_E);
W2 = eye(p+Mode_E)*UC*UC/(p+Mode_E);
for k = 1:ur,
if ~Mode_E,
% w/o mean term
d = U(k,:);
else
% w/ mean term
d = [1,U(k,:)];
end;
if ~any(isnan(d)),
CC = (1-UC)*CC + UC*(d'*d);
%K = (1+UC)*IR*d'/(1+(1+UC)*d*IR*d');
v = IR*d';
%K = v/(1-UC+d*v);
if arg5==0; % original version according to [1], can become unstable
IR = (1+UC)*IR - (1+UC)/(1-UC+d*v)*v*v';
elseif arg5==1; % this provides IR*CC == I, this seems to be stable
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + sum(diag(IR))*W;
elseif arg5==6; % DEFAULT:
IR = ((1+UC)/2)*(IR+IR') - ((1+UC)/(1-UC+d*v))*v*v';
% more variants just for testing, do not use them.
elseif arg5==2;
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + sum(diag(IR))*W2;
elseif arg5==3;
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + eps*eye(p+Mode_E);
elseif arg5==4;
IR = (1+UC)*IR - (1+UC)/(1-UC+d*v)*v*v' + eps*eye(p+Mode_E);
elseif arg5==5;
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + eps*eye(p+Mode_E);
end;
end;
%D(k) = det(IR);
D(k,:) = IR(:)';
end;
IR = IR / UC;
if Mode_E,
IR(1,1) = IR(1,1) - 1;
end;
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