noisedm.m

模糊逻辑工具箱 模糊逻辑工具箱 模糊逻辑工具箱 模糊逻辑工具箱 模糊逻辑工具箱

function slide=noisedm
% This is a slideshow file for use with playshow.m and makeshow.m
% Too see it run, type 'playshow noisedm', 

% Copyright (c) 1994-98 by The MathWorks, Inc.
% $Revision: 1.6 $
if nargout<1,
  playshow noisedm
else
  %========== Slide 1 ==========

  slide(1).code={
   'time = (0:0.01:6)'';',
   'x = sin(40./(time+0.01));',
   'subplot(''Position'', [.1 .75 .3 .2]), plot(time, x);',
   'subplot(''Position'', [.1 .48 .3 .2]), plot(time, randn(size(x)));',
   'domain = linspace(-2, 2, 20);',
   '[xx, yy] = meshgrid(domain, domain);',
   'zz = 4*sin(xx).*yy./(1+yy.^2);',
   'subplot(''Position'', [.45 .52 .3 .35]), mesh(xx, yy, zz);',
   'set(gca, ''box'', ''on'');' };
  slide(1).text={
   '',
   ' Press the "Start" button to see a demonstration of adaptive',
   ' nonlinear noise cancellation using ANFIS.',
   '',
   ' This demo uses Fuzzy Logic Toolbox functions ANFIS and',
   ' GENFIS1.',
   ''};

  %========== Slide 2 ==========

  slide(2).code={
   's=findobj(gcf, ''Type'', ''axes'');',
   'delete(s);',
   'subplot(''Position'',[0.10 0.47 0.65 0.45])',
   'plot(time, x)',
   'title(''Information Signal x'')',
   'xlabel(''time'')',
   'ylabel(''x'')' };
  slide(2).text={
   ' Defined below is a hypothetical information signal x sampled',
   ' at 100Hz over 6 seconds.',
   '',
   ' >> time = (0:0.01:6)'';',
   ' >> x = sin(40./(time+0.01));'};

  %========== Slide 3 ==========

  slide(3).code={
   'n1 = randn(size(time));',
   'plot(time, n1)',
   'title(''Noise Source n1'')',
   'xlabel(''time'')',
   'ylabel(''n1'')',
   '' };
  slide(3).text={
   ' Unfortunately, the information signal x cannot be measured',
   ' without an interference signal n2, which is generated from',
   ' another noise source n1 via a certain unknown nonlinear',
   ' process.',
   '',
   ' Shown in the window is the noise source n1.',
   ' >>n1 = randn(size(time));'};

  %========== Slide 4 ==========

  slide(4).code={
   'domain = linspace(min(n1), max(n1), 20);',
   '[xx, yy] = meshgrid(domain, domain);',
   'zz = 4*sin(xx).*yy./(1+yy.^2);',
   'subplot(''Position'',[0.10 0.47 0.65 0.45])',
   '',
   'surf(xx, yy, zz);',
   'xlabel(''n1(k)'');',
   'ylabel(''n1(k-1)'');',
   'zlabel(''n2(k)'');',
   'title(''Unknown Channel Characteristics That Generate Interference'');',
   'axis([min(domain) max(domain) min(domain)  max(domain) min(zz(:)) max(zz(:))]);',
   'set(gca, ''box'', ''on'');' };
  slide(4).text={
   ' The interference signal n2 that appears in the measured',
   ' signal is assumed to be generated via an unknown nonlinear',
   ' equation:',
   '',
   '                 n2(k) = 4*sin(n1(k))*n1(k-1)/(1+n1(k-1)^2)',
   '',
   ' This nonlinear characteristic is shown as a surface in the',
   ' window.'};

  %========== Slide 5 ==========

  slide(5).code={
   'n1d0 = n1;              % n1 delay 0',
   'n1d1 = [0; n1d0(1:length(n1d0)-1)]; % n1 delay 1',
   'n2 = 4*sin(n1d0).*n1d1./(1+n1d1.^2);    % interference',
   'axis_limit = [min(time) max(time) min([n1;n2]) max([n1;n2])];',
   'subplot(''Position'', [.06 .75 .7 .2])',
   ' plot(time, n1);',
   'ylabel(''noise n1''); axis(axis_limit);',
   'subplot(''Position'', [.06 .45 .7 .2]),',
   ' plot(time, n2);',
   'ylabel(''interference n2''); axis(axis_limit);',
   '' };
  slide(5).text={
   ' The noise source n1 and interference n2 are shown together.',
   ' Note that n2 is related to n1 via the highly nonlinear process',
   ' shown previously; it is hard to see if these two signals are',
   ' correlated in any way.'};

  %========== Slide 6 ==========

  slide(6).code={
   'm = x + n2;             % measured signal',
   'subplot(''Position'',[0.10 0.47 0.65 0.45])',
   'plot(time, m)',
   'title(''Measured Signal'')',
   'xlabel(''time'')',
   'ylabel(''m'')',
   '' };
  slide(6).text={
   ' The measured signal m is the sum of the original information',
   ' signal x and the interference n2. However, we do not  know',
   ' n2. The only signals available to us are the noise signal n1',
   ' and the measured signal m, and our task is to recover the',
   ' original information signal x. In the demo window is the',
   ' measured signal m that combines x and n2.'};

  %========== Slide 7 ==========

  slide(7).code={
   '' };
  slide(7).text={
   ' We will use the function ANFIS to identify the nonlinear',
   ' relationship between n1 and n2. Though n2 is not directly',
   ' available, we can take m as a "contaminated" version of n2',
   ' for training.  Thus x is treated as "noise" in this kind of',
   ' nonlinear fitting.',
   '',
   ' Here we assume the order of the nonlinear channel is known',
   ' (in this case, 2), so we can use 2-input ANFIS for training.',
   '',
   ' Click "Next" to start ANFIS training.'};

  %========== Slide 8 ==========

  slide(8).code={
   'delayed_n1 = [0; n1(1:length(n1)-1)];',
   'trn_data = [delayed_n1 n1 m];',
   'mf_n = 2;',
   'ss = 0.2;',
   'in_fismat=genfis1(trn_data, mf_n);',
   'out_fismat = anfis(trn_data, in_fismat, [nan nan ss]);',
   'estimated_n2 = evalfis(trn_data(:, 1:2), out_fismat);',
   'estimated_x = m - estimated_n2;',
   '' };
  slide(8).text={
   ' We assign two membership functions to each input, so the',
   ' total number of fuzzy rules for learning is 4. Also we set the',
   ' step size equal to 0.2. You should be able to see all the',
   ' training information in the MATLAB command window.',
   '',
   ' >> delay_n1 = [0; n1(1:length(n1)-1)];',
   ' >> trn_data = [delayed_n1 n1 m];',
   ' >> mf_n = 2;',
   ' >> in_fismat=genfis1(trn_data, mf_n);',
   ' >> stepsize = 0.2;',
   ' >> out_fismat = anfis(trn_data, in_fismat, [nan nan stepsize]);'};

  %========== Slide 9 ==========

  slide(9).code={
   'axis_limit =  [min(time) max(time) min([n2; estimated_n2]) max([n2; estimated_n2])];',
   'subplot(''Position'', [.06 .75 .7 .2])',
   ' plot(time, n2)',
   'ylabel(''n2 (unknown)''); axis(axis_limit);',
   '',
   'subplot(''Position'', [.06 .45 .7 .2]),',
   ' plot(time, estimated_n2)',
   'ylabel(''estimated_n2''); axis(axis_limit);',
   '' };
  slide(9).text={
   ' After training, the estimated n2 is calculated using the',
   ' command EVALFIS. The original n2 and estimated n2',
   ' (output of ANFIS) are shown above. (Note that n2 is unknown.)',
   '',
   ' >> estimated_n2 = evalfis(trn_data(:, 1:2), out_fismat);'};

  %========== Slide 10 ==========

  slide(10).code={
   'axis_limit =  [min(time) max(time) min([x; estimated_x]) max([x; estimated_x])];',
   'subplot(''Position'', [.06 .75 .7 .2])',
   'plot(time, x)',
   'ylabel(''x (unknown) ''); axis(axis_limit);',
   'subplot(''Position'', [.06 .45 .7 .2]),',
   '',
   ' plot(time, estimated_x)',
   'axis(axis_limit); ylabel(''estimated_x'')',
   '' };
  slide(10).text={
   ' The estimated information signal x is equal to the difference',
   ' between the measured signal m and the estimated',
   ' interference (that is, ANFIS output).',
   ' >> estimated_x = m - estimated_n2;',
   '',
   ' The original information signal x and the estimated x by',
   ' ANFIS are plotted. Without extensive training, the ANFIS',
   ' can already do a fairly good job.',
   '',
   ' Hit "Reset" to start the demo again.'};
end