WienIdentForAnyNonRoleInOutChangePolyKernel - % Kernel...

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%% Kernel Matrix : Polynomial %% Wiener model identification for any type of nonlinearity: u =c + a*randn %% will be used as input to the system. The results seems to be %% satisfactory for denumerator parameters but not for numerator %% parameters. Actually the role of inputs and outputs are changed here. %% Should it be changed or can it be considered as a hammerstein in some %% ways?. Consider this. clear all u=.5*normrnd(0,2,1,700) ; %.1111; % A white gaussian input sequence u with length %700 0 mean and standard deviation 2 %u=8*rand(1,700)-4; %ut=normrnd(0,2,1,200); %input for testing. e=.1*normrnd(0,.2,1,700); % A white gaussian with zero mean and standart de %viation .2 with length 700. it is error term e = zeros(1,700); % 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] ; N=200; r=7; m=3; sg = 20; % bi s % now we will get the input output data. [h,tt] = impz(b,[a]);
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This note was uploaded on 07/04/2011 for the course ECE 501 taught by Professor Deniz during the Spring '11 term at Istanbul Universitesi.

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WienIdentForAnyNonRoleInOutChangePolyKernel - % Kernel...

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