WienerforMainIdentification - Main Regression Simulation...

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% Main Regression Simulation %% check for the sg value of K matrix and kernel_matrix functions. they are %% changed but now they are the same. clear all u=1*normrnd(0,2,1,700); % 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=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 % been done before a = [2.789 -4.591 5.229 -4.392 2.553 -.8679] ; % ai s b = [1 .8 .3 .4] ; N=200; r=7; m=3; sg = 1/sqrt(2); % bi s % now we will get the input output data. The last 200 datapoints will be % used for training [h,tt] = impz(b,[1 -a]); %filter impulse response us = [0 u(1:end-1)]; % past values of "u" v = sinc(u ).*u.^2; y = conv(h,v)'; figure(2) ; plot(y(1:700)); title('y for little code') %% solve linear equation % construct Kernel matrix . The last two hundred data points will be used. xtrain = u(201:400);u=u+e; for i=1:200 % K is omega matrix for j=1:200 K(i,j) = exp(-((u(1,i+200)-u(1,j+200))^2)/(1*sg^2)); %itis oki end end % Construct Yf. Again the last two hundred data points will be used .
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WienerforMainIdentification - Main Regression Simulation...

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