NonStaticHammerIdent

NonStaticHammerIdent - % Main Regression Simulation % clear...

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% Main Regression Simulation %% clear all L = 900; % Simulation runs this much. u=1*normrnd(0,1,1,L); % A white gaussian input sequence u with length %L 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testing. e=normrnd(0,.1,1,L); % A white gaussian with zero mean and standart de %viation .2 with length L. it is error term e = zeros(1,L); % this is added after all. actually it should have ic = i; % been done befor rts = [.94*exp(1.*ic) .94*exp(-1.*ic) .97*exp(3.6*ic) .97*exp(-3.6*ic) . 95*exp(2.4*ic) .95*exp(-2.4*ic)]; a = poly(rts); %a = [1*[2.789 -4.591 5.229 -4.392 2.553 -.8679]] ; % ai s a = poly(rts); rts_b =[.932 .89*exp(.63*pi*j) .89*exp(-.63*pi*j) ] b = poly(rts_b); N=300; r=8; m=3;n = 6; sg = 1; % bi s b = [1 .8 .3 .4] ; % now we will get the input output data. The last 200 datapoints will be % used for training [h,tt] = impz(b,[a]); %filter impulse response us = [0 u(1:end-1)]; % past values of "u" v = sinc(u + .9*us).*(u +.9*us); %;% y = conv(h,v); figure(4) ; plot(y(1:400)); title('y for little code')
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NonStaticHammerIdent - % Main Regression Simulation % clear...

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