ica.m

这是盲信号的代码 都已经通过编译了 做这方面的同仁可以参考一下 我觉得蛮惯用的

function [Y,W,P] = ica(X,G,opts)
%Independent component analysis
% [Y,W,P] = ica(X,G,opts)
%
%Input parameters
% X : Matrix with original components in the columns.
% G : Nonlinearity used in nongaussianity estimate: (optional)
% 'kurt' G(y) = y^4
% 'cosh' G(y) = log(cosh(y))
% 'gauss' G(y) = -exp(-1/2*y^2) (default)
% opts : Vector with three elements. The first two are used in stopping
% criteria for each component: (optional)
% Number of iterations>opts(1) or
% 1-abs(w'*wOld)<opts(2). (Dot product close to 1).
% opts(3) decides how decorrelation is performed. If the difference
% (one-norm) between two rows in W is less than or equal to opts(3),
% one of them is replaced by a decorrelated version. Use opts(3)=inf
% to decorrelate after all iterations. Decorrelation gives less
% nongaussianity, but prevent two components from being equal.
% Default is opts = [100,10e-6,1]
%
%Output parameters
% Y : Matrix with independent components in the columns.
% W : Transformation matrix so that Y = (W*X')'
% P : Convergence. Sum of abs(w'*wOld) for all components in all iterations.
%
%2002-12-04 | Michael Vinther | [e-mail]

if nargin<2
G = 'gauss';
end
if nargin<3
opts = [100,10e-6,1];
end
% Centering
m = mean(X)';
Xc = X'-repmat(m,1,size(X,1)); % Center and transpose
% Whitening
R = cov(Xc');
[V,D] = eig(R);
WhiteT = V*diag(diag(D).^-0.5)*V'; % Whitening transform
Xw = WhiteT*Xc;
% Select G
switch lower(G)
case 'kurt'
g = inline('x.^3'); gg = inline('3*x.^2');
case 'cosh'
g = inline('tanh(x)'); gg = inline('1-tanh(x).^2');
case 'gauss'
g = inline('x.*exp(-0.5*x.^2)');
gg = inline('exp(-0.5*x.^2)-x.^2.*exp(-0.5*x.^2)');
otherwise
error('Illegal value for G');
end

% Estimate W by FastICA
if nargout>2
[Ww,P] = fastica1(Xw,opts,g,gg);
else
Ww = FastICA1(Xw,opts,g,gg);
end
% Reconstruct
W = Ww*WhiteT;
Y = (W*X')';
%Support function for ica.m. fastica1 is an implementation of algorithm (16) with the
%possibility of turning off decorrelation close to the optimum.