HammersteinFilterOrder

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

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
%% 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);
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 3

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

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online