WienIdentification

WienIdentification - % Identification of Wiener model. %%

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Unformatted text preview: % Identification of Wiener model. %% ------------------------------------------------------------------------ clear all c u=normrnd(0,2,1,400); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testing. e=normrnd(0,.2,1,400); % A white gaussian with zero mean and standart de %viation .2 with length 400. it is error term zt =zeros(1,400); a = [2.09 -2.063 1.209 -.4656 .1164 -.02975] ; % ai s b = [1 .3 -.8 .7] ; N=200; r=7; m=6; % sg = 5; % bi s % now we will get the input output data. The last 200 datapoints will be % used for training % for t = 1:400 if (t==1) z(1,t) = b(1,t)*u(1,1); y(1,t) = 5*(z(1,t)); % following was before: sinc(z(1,t))*z(1,t)^2 ;% no +e(1,t); % b(1,t)*sinc(u(1,1))*(u(1,1)^2) + e(1,t); end % (exp(.3*z(1,t))-exp(-.3*z(1,t)))/(exp(.3*z(1,t))+exp(-.3*z(1,t))) if (1<t&&t<=6) sm1 = 0; for i = 1:t-1 sm1 = sm1 + a(1,i)*z(1,t-i); % sm1 = sm1 + a(1,i)*y(1,t-i); end sm2 = 0; if(t<4) for j = 1:t sm2 = sm2 + b(1,j)*u(1,t-j+1);...
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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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WienIdentification - % Identification of Wiener model. %%

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