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HammersteinFilterOrder

# HammersteinFilterOrder - Main Simulation clear all ic = i...

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%% Main Simulation %% clear all ic = i; Nt = 800 ;cclass = 0; % N: # of input datas, cclass : # of correct class (:p value) for epoch = 1:5 u=.2*normrnd(0,2,1,Nt); % A white gaussian input sequence u with length %Nt 0 mean and standard deviation 2 % u = rand(1,Nt) +1; 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 e = zeros(1,400); % this is added after all. actually it should have % 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)... .94*exp(1.2*ic) .94*exp(1.2*ic)]; aa = poly(rts); a = -aa(2:end); a1st = [2.789 -4.591 5.229 -4.392 2.553 -.8679] ; % ai s b = [1 .9 ] ; % bi s err = zeros(10,1); [h,tt] = impz(b,[1 -a]); %filter impulse response %v = sinc(u).*u.^2; y = conv(h,u(1:Nt)); for n = 1:10 for m=0:0 N = 200; sg = 2; r = n+m+1; % n = 8; m = 5; % N: # of training data K = zeros(N,N);Ker = zeros(N,200); x = zeros(n+m+1,Nt); xr = zeros(n+m+1,Nt); %regression vectors. % now we will get the input output data. The last N datapoints will be % used for training % for t = 1:Nt % if (t==1) % y(1,t) = b(1,t)*u(1,1) ; % sinc(u(1,1))*(u(1,1)^2) + e(1,t); % % end % % if (1<t&&t<=6) % sm1 = 0; % for i = 1:t-1 % sm1 = sm1 + a(1,i)*y(1,t-i); % end % sm2 = 0; %

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HammersteinFilterOrder - Main Simulation clear all ic = i...

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