WienerAsAHammersteinAnyNon

WienerAsAHammersteinAnyNon - % Wiener identification :...

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%% Wiener identification : thinking it as a Hammerstein model. Using small %% signal analysis. here a stepwise constant is added to input. But the %% results seem to be nice for d %% transient time. Increasing the training data gave worse results. %% Decreasing it below some points also gave worse results. clear all u=.35*normrnd(0,.32,1,700) + 24; % A white gaussian input sequence u with length %700 0 mean and standard deviation 2 %u=8*rand(1,700)-4; e=.05*normrnd(0,.2,1,1189); % A white gaussian with zero mean and standart de % viation .2 with length 700. it is error term %e = zeros(1,1189); % this is added after all. actually it should have ic = i; % been done before rts = [.98*exp(ic) .98*exp(-ic) .98*exp(1.6*ic) .98*exp(-1.6*ic). .. .95*exp(2.5*ic) .95*exp(-2.5*ic)]; a = poly(rts); % ai s b = [1 .8 .3 .4] ; % bi s % now we will get the input output data. [h,tt] = impz(b,[a]); %filter impulse response us = [0 u(1:end-1)]; % past values of "u" v = conv(h,u); v2 = (sin(u).*u) ; y2 = conv(h,v2);figure(50); plot(u,v2,'r+');title('v2 vs u . hammersteinish '); y =(sin(v).*v)+e; % y = conv(h,v2); % 3*(-.5 + 1./(1 + exp(-.5*v)));%2*v; y_y2diff = y-y2;figure(51);subplot(4,1,4); plot(y_y2diff(1:700)); title('y-y2: wiener output-hammerstein output') figure(1);subplot(3,1,1) ; plot(u(1:700)); title('input to the system'); subplot(3,1,2) ; plot(v(1:700)); title('output of the filter of wiener: before nonlinearity: v'); axis([1 700 -2 12]) subplot(3,1,3) ; plot(y(1:700)); title('output of the whole system of wiener');hold off
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WienerAsAHammersteinAnyNon - % Wiener identification :...

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