Farhang

 FUNCTION Farhang() implements the Farhang-Boroujeny's variable step size CLMS algorithm 

 Based on the paper of Farhang-Boroujeny "A new class of gradient adaptive step-size LMS algorithms",
 IEEE Trans. Signal Processing, vol. 49, no. 4, 2001.

 INPUT:
 x: input signal
 N: filter length
 mu: step-size
 rho: step-size of adaptation of mu

 OUTPUT:
 y: filter output


 Complex Valued Nonlinear Adaptive Filtering toolbox for MATLAB
 Supplementary to the book:
 
 "Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models"
 by Danilo P. Mandic and Vanessa Su Lee Goh
 
 (c) Copyright Danilo P. Mandic 2009
 http://www.commsp.ee.ic.ac.uk/~mandic
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    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 2 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 can obtain a copy of the GNU General Public License from
    http://www.gnu.org/copyleft/gpl.html or by writing to
    Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 ...........................................

0001 % FUNCTION Farhang() implements the Farhang-Boroujeny's variable step size CLMS algorithm
0002 %
0003 % Based on the paper of Farhang-Boroujeny "A new class of gradient adaptive step-size LMS algorithms",
0004 % IEEE Trans. Signal Processing, vol. 49, no. 4, 2001.
0005 %
0006 % INPUT:
0007 % x: input signal
0008 % N: filter length
0009 % mu: step-size
0010 % rho: step-size of adaptation of mu
0011 %
0012 % OUTPUT:
0013 % y: filter output
0014 %
0015 %
0016 % Complex Valued Nonlinear Adaptive Filtering toolbox for MATLAB
0017 % Supplementary to the book:
0018 %
0019 % "Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models"
0020 % by Danilo P. Mandic and Vanessa Su Lee Goh
0021 %
0022 % (c) Copyright Danilo P. Mandic 2009
0023 % http://www.commsp.ee.ic.ac.uk/~mandic
0024 %
0025 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0026 %    This program is free software; you can redistribute it and/or modify
0027 %    it under the terms of the GNU General Public License as published by
0028 %    the Free Software Foundation; either version 2 of the License, or
0029 %    (at your option) any later version.
0030 %
0031 %    This program is distributed in the hope that it will be useful,
0032 %    but WITHOUT ANY WARRANTY; without even the implied warranty of
0033 %    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
0034 %    GNU General Public License for more details.
0035 %
0036 %    You can obtain a copy of the GNU General Public License from
0037 %    http://www.gnu.org/copyleft/gpl.html or by writing to
0038 %    Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA.
0039 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0040 % ...........................................
0041 function y = Farhang(x,N,mu,rho,alpha)
0042 
0043 M = 1;% prediction horizon
0044 L = length(x)-M; % length of simulation
0045 filterinput = zeros(N,L);%input of FIR
0046 filteroutput = zeros(1,L);%output of FIR
0047 learning = zeros(1,L);% the adaptive learning rate;
0048 Phi = zeros(N,1);
0049 WVSLMS = zeros(N,1);% weight
0050 eVSLMS = zeros(1,L);% error
0051 EVSLMS = zeros(1,L);% mean square error
0052 filteroutput = zeros(1,L);% output
0053 
0054 
0055 for i = 1:L
0056     for m = 1:N
0057         if (i-m+1)>0
0058             filterinput(m,i) = x(1,i-m+1);
0059         else
0060             filterinput(m,i) = 0;
0061         end
0062     end 
0063     filteroutput(i) = transpose(filterinput(:,i)) * WVSLMS;%
0064     eVSLMS(i) = x(i+M) - filteroutput(i);%
0065     EVSLMS(i) = 10 * log10(1/2 * eVSLMS(i)' * eVSLMS(i));
0066     if i == 1
0067         learning(1) = mu;
0068         Phi = zeros(N,1);
0069     else
0070         Phi =  alpha * Phi + ...
0071                 eVSLMS(i-1) * conj(filterinput(:,i-1));
0072         learning(i) = learning(i-1) + rho * real(eVSLMS(i) * filterinput(:,i)'* conj(Phi));
0073     end
0074     WVSLMS = WVSLMS + learning(i) * eVSLMS(i) * conj(filterinput(:,i));
0075 end
0076 y = filteroutput;
0077 
0078 
0079 
0080